Printer Friendly

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining.

1. Introduction

Clinical pathway (CP) is of great importance in disease treatment, hospital management, and government regulation. It has been widely used to make the treatment process more standardized and normalized. The core principle of CP is to divide the clinical treatment into several stages and detail the clinical activities in each stage. Now, there are more than 300 CPs which have been designed by experts and implemented in China. Table 1 is a Chinese CP example about intracerebral hemorrhage (ICH). However, these CPs can be hardly put into practical application due to their static and nonadaptive feature.

Recently, clinical pathway mining (CPM) has experienced increased attention, which refers to using data mining technologies to discover the execution CP from hospital historical data. In comparison with the expert-designed CP, the data-driven execution CP is more objective. It can facilitate the CP (re)design, CP implementation, and abnormal detection.

The most current CPM approaches are based on the process mining technologies, which are popular in extracting process models from event logs. The resultant graph models contain two basic elements: nodes represent clinical activities and edges represent the order relations between these activities. However, the clinical data are too unstructured and complex for process mining methods. They usually result in spaghetti-like models which are comprised by massive nodes and edges. To make the process model more comprehensive, researchers attempt to manually predefine the key activities for process mining. It is effective in promoting the model conciseness and highlighting core information, while the manual works are always expensive and subjective.

Some other approaches focus on using topic modeling methods to discover clinical patterns instead of process models. They assume that the treatment for a disease can be concluded into a number of patterns. For a patient, a doctor would choose one pattern for him/her according to the symptoms. To achieve this target, topic modeling technologies, such as latent Dirichlet allocation (LDA) [1] and its variants, are applied on the clinical data [2-5]. They model each patient trace as mixtures of multiple topics, while each topic is modeled as a multinomial distribution over clinical activities. However, these approaches can hardly reflect the temporal structure between clinical activities, which are important for CP.

In our previous work [6], we take the advantages of both topic modeling and process mining to generate a concise and interpretable topic-based process model. The key principle is to use LDA to discover the latent topics from the clinical data and extract the order relations between these topics by process mining methods. It is suitable to regard the latent topics as the clinical goals based on the following two clinical practices:

(1) The clinical activities occurring in a specific time duration (usually a hospitalized day) are prescribed around several clinical goals.

(2) Each clinical goal corresponds to a clinical activity set.

Take the first day of ICH as the example (as shown in Table 1). For a new patient, the doctors would make a diagnosis for him/her by evaluating the intracranial pressure (ICP) and try to control the ICP into the normal range. These constitute the clinical goals for the first day. To achieve these goals, doctors have a series of corresponded choices. Brain CT and the related medical imaging are suitable for the former goal. Dehydration drugs like mannitol and glycerin fructose are very useful for the latter goal. Thus, each clinical day would be represented as several topics instead of the detailed clinical activities. Moreover, the process mining method is effective in discovering the temporal structure in these high-level sequences.

However, the raw LDA can hardly ensure the quality of the discovered topics, which is critical to the interpretability of the final process model. By carefully analyzing the clinical scenario, we find two common problems in applying raw LDA on clinical data.

(1) The same clinical activities occurring in one clinical day may be assigned different topics.

(2) A clinical activity may have strong correlation to many topics.

We demonstrate two examples to explain them: (1) In Figure 1(a), the four penicillin prescribed by the doctor in one clinical day are assigned to four different topics; while in the clinical scenario, they are both used for the same clinical goal, which means that they should share the same topic. (2) In Figure 1(b), the clinical activity syringe ranks high in all the three discovered topics. However, each clinical activity should have relative limited number clinical goals rather than various clinical goals.

To address the above problems, we proposed a novel extension of LDA for CPM, which is called Clinical Daily Goal LDA (CDG-LDA). As shown in Figure 2, we first introduce a constraint into LDA which can ensure the same clinical activity in one clinical day would be assigned to the same topic (topic assignment constraint). It can improve the consistency of the topic assignment procedure for clinical goal discovering. Second, we present an efficient method to adaptively limit the topic number of each clinical activity and adjust its ranking in the topic according to their informativeness (topic correlation limitation). It guarantees that a clinical activity would only rank high in limited clinical topics. Third, an effective process mining framework is applied on the topic-based sequences to get a comprehensive process model. It is worth mentioning that we use the billing data as our experimental data. Compared to other various kinds of clinical data, billing data is the most common and credible one due to its importance in insurance area. And it can be easily extracted from hospital information system without complex data integration technologies. Billing data is also of the lowest data granularity and contains a lot of noise, which brings a great difficulty for CPM task. Thus, the CPM approach on the billing data is both valuable and challenged.

Our main contributions in this paper are as follows:

(i) We incorporate topic assignment constraint into LDA to make the inference procedure consistent for the same clinical activities in one clinical day.

(ii) Informativeness is introduced to adjust the ranking in each topic, which can improve the topic quality.

(iii) Experiments on real-world datasets demonstrate the great performance of our approach for CPM by applying process mining on the quality discovered topics.

We first give the definitions used in this paper:

Definition 1 (clinical activity). A clinical activity is an event occurring at a particular time point, which refers to the basic element in treatment.

Definition 2 (clinical day and patient trace). A patient trace contains a series of clinical activities. We segment each patient trace into a collection of clinical days, and a clinical day represents a set of clinical activities occurring in the same hospitalized day.

2. Related Work

Early in 2001, Lin et al. [7] pointed out the shortage of the expert-designed CPs in handling the great variances caused by individual differences. So that a more dynamic and adaptive process is needed for improving the performance of CPs. They proposed a graph mining technique to extract the time dependency pattern of CPs for curing brain stroke. The pattern can be used for predicting the paths for a new patient. In [8], hidden Markov model (HMM) was adopted for inferring the CP pattern, which was useful in knowledge sharing and CP updating. While these methods were severely limited to the data deficiency and complexity and because CPs focus on the order relations between various clinical events, process mining technologies are widely used for CPM. A case study on a group of 627 gynecological oncology patients was presented in [9] by using heuristics miner [10], which resulted in a spaghetti-like process model. Lang et al. [11] evaluated the performance of seven traditional process mining methods on clinical data. The results demonstrated that these methods can hardly obtain comprehensive process models on clinical scenario.

Researchers attempted to adopt frequency-based methods to improve the ability of analyzing the complex process models. In [12], the patient traces with similar schemas and outcomes are grouped together. In [13], sequence clustering was used to identify regular behavior, process variants, and infrequent behavior on the process model discovered by process mining method. Zhang et al. [14] proposed a frequency-based method to group the patients into several predefined core dimensions. Lakshmanan et al. [15] presented a CPM framework that combined clustering, process mining, and frequent pattern mining technologies to mine an outcome-related process model. In [16], a segmentation method was proposed to discover the frequent behavior patterns with different time intervals. CareExplorer [17] was a novel CP management tool which combined frequent sequence mining techniques with advanced visualization supports. Manually defining a set of concerned and important clinical activities can significantly improve the interpretability of the process models [18-20]. However, manual works are always subjective and expensive for CPM.

Considering the complexity and quantity of clinical data, topic modeling technologies are used to discover the clinical patterns, which are useful in CP (re)design and abnormal detection. Huang et al. [2] firstly introduced LDA to extract latent topics as the treatment patterns from clinical data. Each patient trace was regarded as a mixture of different treatment patterns. In addition, the team integrated time stamps [3], patient features [4], and comorbidities [5] into LDA to enhance the ability of summarizing patterns. However, treating a patient trace as a whole can hardly represent the feature of CP that the treatment process is consisted of several stages with different clinical goals. To deal with this problem, our previous work [6] puts the emphasis on discovering the clinical goals from clinical data by LDA and deriving the order relations between these goals by process mining method. It was effective in generating a concise, ordinal, and interpretable process model as CP. While as discussed in [21], the performance of this approach is quite limited to the effectiveness of LDA.

In this paper, we extend the work in [21] to enhance the ability of discovering quality clinical goals by incorporating the topic assignment constraint and topic correlation limitation into LDA.

3. Methodology

We first introduce the conversion from billing data to the word-count format for LDA. Then, we present a brief review of applying LDA on clinical data. After analyzing the two shortages of the raw LDA, we incorporate topic assignment constraint and topic correlation limitation into LDA to improve the performance of discovering latent topics from clinical data.

3.1. Data Preparation. Billing data is the most common data recorded in hospital information system (an example is shown in the upper-left part of Figure 2). Four basic attributions are essential for the topic modeling: trace ID (the identifier for one patient trace), item name, amount, and occurrence time (in the unit of day). Each billing item (one row in the table) represents a collection of clinical activities, such as the first row <A, 3> means there are three clinical activity A. Note that the amount of different kinds of items should be normalized into the same scale first. We denote the clinical activity domain as the vocabulary. The billing items with the same trace ID and occurrence time constitute one clinical day. With respect to LDA, the clinical day with clinical activities corresponds to the document with words.

3.2. Brief Introduction for LDA. LDA is one of the most popular statistical topic modeling technologies. It models the generative process of each word from each document in a text dataset. There are two core model parameters in LDA: a corpus-level distribution over words for each topic and a document-level distribution over topics for each document. In clinical data, by regarding the clinical day and clinical activity as the document and word, respectively, the generative process of LDA has great conformity with the two abovementioned clinical practices. The graphical representation is shown in Figure 3(a), and the notations are explained in Table 2. The generative process of LDA is as follows:

(1) Draw distribution [[phi].sub.k] ~ Dir([beta]), k =1, 2, ..., K

(2) For dth clinical day, d = 1, 2, ..., D

(a) Draw distribution [[theta].sub.d] ~ Dir([alpha])

(b) For ith clinical activity in dth clinical day, i = 1, 2, ..., [N.sub.d]

(i) Draw [z.sub.d,i] ~ Multi([[theta].sub.d])

(ii) [mathematical expression not reproducible]

According to the graphical model, we need an inference to find the parameters which can best match the observed clinical data. Gibbs sampling, a special case of Markov chain Monte Carlo (MCMC), is the most popular inference method of LDA. It is based on the joint distribution of latent topics and observed clinical activities as follows:

[mathematical expression not reproducible], (1)

where [GAMMA](x) is the gamma function.

Given the joint distribution, the topic assignment for each clinical activity can be written as follows:

[mathematical expression not reproducible]. (2)

After the Gibbs sampling, the two important hidden parameters A and Q are computed as follows:

[mathematical expression not reproducible], (3)

where [[phi].sup.(v).sub.k] is the probability of clinical activity v which belongs to topic k and [[theta].sup.(k).sub.d] is the probability of the dth clinical day which contains topic k.

3.3. Topic Assignment Constraint. We can see that the inference procedure is mutually independent for all the clinical activities (as shown in (2)). So that even the same clinical activities in one clinical day may get different topics, which violates to the clinical practice.

In this section, we adopt the chain graph proposed in [21] to incorporate topic assignment constraint into LDA. It can coerce the same clinical activities on one clinical day to take on the same topic. The graphical model is shown in Figure 3(b). In each clinical day, we first put all the same clinical activities into a group and then use indirect edges to connect them. A function [C.sup.g.sub.d] is imposed to express the topic assignment constraint as follows:

[mathematical expression not reproducible]. (4)

For each group, [C.sup.g.sub.d] would equal to 1 only when all the clinical activities in the group get the same topic. Our goal is to make all the groups conform to the constraint. So that we add [C.sup.g.sub.d] in the joint distribution of LDA (1) as follows:

[mathematical expression not reproducible], (5)

where [DELTA] is the normalization constant.

Similar to the Gibbs sampling algorithm for LDA, we sample a topic for a group based on its posterior P'([z.sup.g.sub.d] - k\[mathematical expression not reproducible]), where [z.sup.g.sub.d] = k represents the set [z.sup.g.sub.d] which has the same topic k (the notations are explained in Table 2).

[mathematical expression not reproducible]. (6)

3.4. Topic Correlation Limitation. The ranking of a clinical activity v in a topic k is determined by [[phi].sup.(v).sub.k]. From (3), it is observed that [N.sup.(v).sub.k] is critical to the calculation of [[phi].sup.(v).sub.k]. In addition, the sum of [N.sup.(v).sub.k] among all topics is a fixed value, which equals to the frequency of clinical activities v (denoted as [N.sup.(v)]) in all clinical days. Due to the independence of the inference procedure of LDA, the frequency of v may be uniformly allocated to various topics. Thus, a high-frequency clinical activity is much easier to rank high in many topics, while a low-frequency clinical activity can hardly rank high in even one topic. It results in a large redundancy between different topics, so that each topic is lacking of characteristics. It is hard to regard these topics as the clinical goals.

We consider using informativeness feature to solve this problem. Our insight is that for an informative clinical activity, it should rank high in a small number of topics, which means that it can better represent these clinical goals than other uninformative clinical activities. We incorporate topic correlation limitation into LDA to achieve this target. This limitation contains two aspects:

(1) Restrict the distribution of [N.sup.(v)] over topics. Higher informativeness and less relevant topics

(2) Associate the calculation of [[phi].sup.(v).sub.k] with the informativeness of v.

We use inverse document frequency (IDF, see (7)) to measure the informativeness of each clinical activity. In general, informative clinical activities are expected to have higher IDF and hence less relevant topics and higher ranking.

[mathematical expression not reproducible]. (7)

Accordingly, the new calculation of [[phi].sup.(v).sub.k] is denoted as follows:

[[bar.[phi]].sup.(v).sub.k] = [[phi].sup.(v).sub.k] x IDF(v). (8)

The core principle for the first aspect is to keep discarding the most irrelevant topic for a clinical activity. It is achieved by maintaining a relevant topic list for each clinical activity during the iteration of Gibbs sampling. The pseudocode for this procedure is shown in Algorithm 1.

The topic number upper bounder refers to the maximum number of the topics a clinical activity can correlate to. For example, given a clinical activity ECG and its topic number upper bounder s(ECG) - 3, its [N.sup.(v)] can be only allocated to three topics. We set from 1 to K according to its IDF. Then, for each clinical activity value v, we maintain a corresponding relevant topic list [kappa](v). In every d iteration, we check whether the size of [kappa](v) has been reduced to the desired number (line 12). To the clinical activities which still need to be restricted, we will update their lists (line 13) by discarding the topic with minimum relevant value (RV) as defined in

RV(k,v) = [N.sup.(v).sub.k]/rank(k,v), (9)

where rank(k,v) is the ranking of clinical activity v in topic k based on the multinomial distribution [[phi].sub.k]. The numerator [N.sup.(v).sub.k] reflects the relevant degree of topic k to clinical activity v. And the denominator rank(k,v) reflects the importance of clinical activity v to topic k. Thus, higher RV represent more correlation between the clinical activity and the topic.
ALGORITHM 1: Gibbs sampling of CDG-LDA.

Input: A dataset of D clinical days.
         Topic number K.
         Hyper-parameters [alpha] and [beta].
         Iterations [delta] for updating relevant topic list.
Output: The multinomial distribution [phi] and [theta].
1 Calculate IDF for each clinical activity;
2 Initialize the topic number upper bounder s(v) [member of] [1,K]
  for clinical activity with value v according to its IDF;
3 Initialize the relevant topic list [kappa](v) = {1,2, ..., K} for
  clinical activity with value v;
4 Sample [z.sup.g.sub.d] for each group, and Initialize all the count
  parameters [N.sub.k], [N.sup.(k).sub.d] and [N.sup.(v).sub.k]
  accordingly;
5 for l =1 to K do
6      if l > 1 then
7        for v =1 to V do
8          if (K - l + 1) [greater than or equal to]s(v) then
9            Update [kappa](v);
10      for r = 1 to [delta] do
11        for d = 1 to D do
12          for g = 1 to [G.sub.d] do
13            k = [z.sup.g.sub.d];
14            [N.sub.k] - = [A.sup.g.sub.d],[N.sup.(k).sub.d] - =
              [A.sup.g.sub.d],[N.sup.(v).sub.k]  - = [A.sup.g.sub.d];
15            Sample topic index k' from [kappa](v)
              according to (6);
16            [N.sub.k'] + = [A.sup.g.sub.d],[N.sup.(k).sub.d] + =
              [A.sup.g.sub.d],[N.sup.(v).sub.k'] + = [A.sup.g.sub.d];
17            Calculate and return [phi] (based on 8) and [beta];


By keeping on updating the relevant topic lists, we gradually reduce the dimension of topic selection to the desired range. For instance, suppose there are K =5 topics and a clinical activity ECG with a RV vector {50, 150, 5, 100, 60} and s(ECG) = 3, the relevant topic list k(ECG) would be changed from {1,2, 3,4, 5} to {1,2, 4, 5} in the next updating procedure. As seen in line 15 of Algorithm 1, we do the topic sampling from relevant topic list instead of all the topics. To the preinstance, it means that we would sample a topic from topics 1, 2, 4, and 5 without topic 3 because of its minimum RV.

The complexity of deriving a sample [z.sup.g.sub.d] is O(K), where K is the number of topics. Therefore, the overall complexity is O(K[delta]GK) = O([K.sup.2][delta]G). [delta] is the number of iteration for updating relevant topic list and G = [[summation].sup.D.sub.d][G.sub.d]. In practice, K would not be a large value and [delta] could be treated as a constant, so that G is the key factor that contributes to the complexity. It means that the proposed method scales linearly as we increase the total number of groups.

3.5. Process Mining. Instead of the detailed and complex clinical activities, we use the discovered topics to represent each clinical day. Hence, each patient can be regarded as a topic-based sequence. We adopt the process mining framework proposed in [6] to derive a concise and interpretable process model.

4. Experiments

In this section, we conducted a series of experiments to demonstrate the effectiveness of the proposed methods in discovering quality CP model. We begin with the description of experimental settings.

4.1. Experimental Settings. To evaluate both the suitability and generality of our approach, we collected two real-world billing datasets from a city-centre hospital. The two datasets are about two different diseases: an internal disease--intracerebral hemorrhage (ICH), and a surgical disease--inguinal hernia (IH). The detailed statistics are summarized in Table 3. We set the Dirichlet prior to [alpha] = 10 and [beta] = 0.01.

The topic number K is determined by a trade-off strategy proposed in [6]. It attempt to find a balance between the perplexity of topic modeling and the topic label size. The former one is commonly used in evaluating the topic models' prediction ability. It is defined as the reciprocal geometric mean of the likelihood of the data. Lower perplexity refers to better predictiveness and hence a better K, while perplexity is not strongly correlated to human judgement [22]. In our approach, the size of topic label is critical to the conciseness of the final process model. High topic number K would significantly reduce the interpretability of the model. Thus, we select the intersection point of the two metrics across various K as the optimal topic number (K = 8 for ICH and K = 5 for IH). The iterations for updating relevant topic list [delta] are set to 300.

4.2. Topic Quality. The latent topics discovered by topic modeling are interpreted as the clinical goals in our approach. To evaluate the quality of the topics, we compare our approach (CDG-LDA) against LDA [6] in three aspects: coherence, redundancy, and coverage. Tables 4 and 5 show the clustering results of the two methods. For each topic, we listed the top N =10 ranked clinical activities and asked doctors to label it. The similar topics generated by different methods are in the same row. Note that, given a top size N, we defined T (total top list) as all the top N-ranked clinical activities among K topics. For example, T refers to the 80 clinical activities for ICH when N =10 and K = 8.

4.2.1. Coherence. We expect that the clinical activities in a topic could represent the similar clinical goal. Take topic 5 (the 5th row in Table 5) as the example. It is observed that the result of LDA contains both surgery- and nursing care-related activities. While in the result of CDG-LDA, most of the clinical activities are about surgery. The similar situations can be found in 2nd, 4th, and 5th rows of ICH and 2nd row of IH. It shows that the result of approach has significant higher coherent degree than LDA. This may be due to the high co-occurrence of these different kinds of clinical activities. For example, the nursing care activities are usually used with some medication activities in one clinical day, and they all have high-frequency in patients' treatment. Based on the mutually independent topic inference procedure of LDA, it is possible to assign the same topic to parts of the two kinds of activities in one clinical day, such as 60% of nursing care activities and 50% of medication activities got the same topic, and the other activities got different topics. By introducing the topic assignment constraint, we guarantee that the two kinds of activities in one clinical day would be assigned either the same topic or different topics. It can improve the consistency of the topic assignment, which is more conformed to the clinical practice.

We proposed a user study to quantitatively measure the coherence degree of different methods. Three doctors were trained with a short tutorial and invited to label the top 20 clinical activities of each topic to very relevant (score 2), relevant (score 1), or irrelevant (score 0). The final score is determined by a majority voting strategy. The kappa value, which is used to measure the interrater agreement, is 0.73 for ICH and 0.74 for IH. We used NKQM@N [23] as the metric to measure the coherence degree.

[mathematical expression not reproducible], (10)

where K is the topic number, [M.sub.k,j] is the jth clinical activity generated by method M for topic k, and [Z.sub.N] is the normalization factor. Higher value of NKQM@N implies better ranking result. As shown in Table 6, the performance of CDGLDA are remarkably better than LDA across various depth of NKQM.

4.2.2. Redundancy. Given two discovered topics, we expect that they would not look alike, which means that the two topics should contain few same clinical activities. Thus, the redundancy indicator focuses on the distinctive characteristic of each topic. We measure the redundancy (RE, see (11)) by calculating the overlapping degree between all the topics.

RE = [[summation].sub.v[member of][OMEGA](T)](country(v) - 1), (11)

where [OMEGA](T) is the unique clinical activities in T and count (v) is the number of the occurrence of clinical activity v in the T (top N clinical activities per topic, K topics). In the previous example in Figure 1(b), the RE value among all the three topics is 2 (suppose N > 3). Lower RE represents less redundancy and hence a better clustering result.

Figure 4 shows the comparison between CDG-LDA and LDA. It is observed that CDG-LDA consistently outperforms LDA across various topic number K and top size N. This is due to the incorporation of topic correlation limitation. By updating the relevant topic list during the iterations of Gibbs sampling, each clinical activity would strongly correlate to limited topics, especially the informative clinical activities. As a sequence, a clinical activity would not rank high in many topics, which lead to a low redundancy degree among all the topics.

4.2.3. Coverage. Coverage refers to the ability of discovering important clinical activities for a specific disease. Given two high coherent and low redundant topics (syringe, infusion apparatus and arterial/venous cannula, hemostix) for ICH, both of them are about the basic clinical equipment. However, these clinical activities are not critical enough for the treatment of ICH. We aim to find out all the key clinical activities in T. We used the necessary/ recommendatory clinical behaviors in the Chinese National Clinical Pathway and Chinese/AHA/ASA/EHS guideline as our benchmark. The overlapping degree between T and the benchmark is adopted as the coverage indicator. Here, we took the T listed in Tables 4 and 5, which means the top size N = 10, to analyze the coverage from three aspects:

(i) Examination

For ICH, the core examinations contains blood/urine routine, ECG, and biochemical and imaging examinations. For the first three, both LDA and CDG-LDA have discovered majority of the related clinical activities in topics 5, 6, 7, and 8. While for the last one, LDA failed to find out brain CT, which is the most effective imaging examination for ICH. In CDG-LDA, brain CT ranked high in topic 5.

For IH, a patient needs the similar examinations to ICH except the imaging test. In topics 1 and 3 of the two methods, we can find the clinical activities about blood/urine routine, ECG, and biochemical examinations. An interesting observation is that the topics in the 2nd row got by LDA and CDG-LDA are quite different. In LDA, the activities in topic 1 are mainly about the infectious disease assay, which is an important part of biochemical examination. While topic 2 of CDG-LDA focuses on the presurgical preparation. Although LDA got better performance in infectious disease examinations, we deem that the result of CDG-LDA is more suitable for the clustering of IH clinical behaviors. There are two reasons: (1) The infectious disease assay-related activities can be found in the top 20 of topic 1 in CDG-LDA. They all belonged to the biochemical examinations which will always be prescribed together in practice, so that it is reasonable to put them in the same topic. (2) Topic 2 about presurgical preparation is of great importance for IH. We would detail this in the following part.

(ii) Treatment

Due to the ICH dataset is extracted from the Neurology Department rather than the Neurosurgery Department, the treatment activities are mainly medical instead of surgical. According to the drug function, the commonly used medication for ICH can be divided into four categories: reducing ICP (dehydration), keeping electrolyte balance, controlling blood pressure, and preventing complications (like dysphagia and aspiration, nervous system injury, bacterial infection, etc.). Most of them have been covered by both LDA and CDG-LDA. However, LDA missed an important dehydration drug piracetam, which is of high frequency in the dataset.

IH is a typical surgical disease, so that the treatment activities are mainly about the surgery. It is observed that both LDA and CDG-LDA contained the surgery-related clinical activities in topic 5. However, LDA failed to discover the most critical surgical activity IH repair and anesthesia in the top 10 of topic 5. This is caused by the high redundancy between topics 4 and 5 of the LDA. It made the ranking of these important surgical activities lower than the nursing care activities, which are of high-frequency. Moreover, as we discussed before, few of presurgical activities have been found by LDA. By limiting the topic number of each clinical activity and adjusting the day-activity distribution calculation, CDG-LDA successfully discovered these clinical activities in topic 4.

(iii) Nursing care

In both ICH and IH datasets, the frequency of the clinical activities about nursing care are always the highest. Hence, the two topic modeling methods got good performance in discovering this kind of activities, including different level nursing care and various nursing equipment.

(1) Discussion of Topic Quality. From the abovementioned three aspects about coherence, redundancy, and coverage, we can tell that CDG-LDA demonstrates significantly better performance than the original LDA in discovering latent topics from clinical data. The extracted high-quality topics can be suitably interpreted as the clinical goals.

4.3. Process Model Demonstration. By using a topic label (contains several topics) to represent each clinical day, we got a series of topic-based sequences. In this section, we demonstrate the process models derived from these sequences by using the process mining methods proposed in [6]. Note that we combined the topics about biochemical examinations, which are always used together, for the sake of discussion. Figures 5 and 6 show the result model of ICH and IH, respectively. We compared them with the Chinese National Clinical Pathways.

(i) ICH

The process can be easily divided into three stages.

(1) Admission (topic (5), topic (6, 7, and 8)): the patient would accept a series of examinations for making a definite diagnosis, including brain CT, ECG, and biochemical assays. Note that imaging examinations are not required for all the traces. This is due to the fact that the clinical activities in our imaging topic (topic (1)), such as CTA and MRA, are mainly used as the auxiliary means for the diagnosis of ICH. Usually, the patients with severe symptoms or comorbidities need further imaging examinations.

(2) Treatment (topic (3), topic (2, 3), topic (4), and topic (2)): After various examinations, the ICH patients would receive nursing care and take medications for recovery. Because of the urgency of ICH, drugs and high-level nursing care are firstly used to relieve and monitor the symptoms. When symptoms become stable, the nursing level may be changed to regular. As an internal department, various medications are used for ICH treatment according to the patients' symptoms.

(3) Re-examination (topic (5), topic (2, 4, and 5), and topic (1)): During the treatment stage, related examinations are repeatedly needed for confirming the patients' states.

(ii) IH

Similar to ICH, the IH patients would firstly receive various examinations. Then, a series of presurgical activities are used for them. Note that some patients would have surgery in the same clinical day to the presurgical preparations (topic (5, 2)), while others may accept surgery in the next clinical day (topic (2)). The average level of nursing care for IH is lower than ICH. And the antibacterial drugs are used during the surgery and nursing care period. It is worth mentioning that the majority of IH patients have undergone the surgical treatment; while it still existed, a small number of IH patients have not received surgery. By careful analysis, we found that these patients are mainly the infants or the elders who are not appropriate for surgery.

(1) Discussion of Process Model. It is observed that the topic-based clinical process model is of great conciseness and interpretability for CPM. We can conveniently draw the execution CP from the clinical historical data and identify the characteristics compared to expert-designed CP.

5. Conclusion

In this paper, we proposed a novel topic modeling approach to discover latent topics as the clinical goals for CPM. The key idea is to incorporate clinical practice into the generative process of LDA to improve the topic quality. Topic assignment constraint restricts that all the same clinical activities in one clinical day should be assigned the same topic. Topic correlation limitation incorporates informativeness feature to adaptively adjust the ranking of the clinical activities in each topic. Process mining methods are used on these topic-based sequences instead of the detailed clinical activities. The experimental results show that our approach outperforms the original LDA in coherence, redundancy, and coverage. And the resultant topic-based process models are of great conciseness and interpretability for CP representation.

One extension of this work is to find a more suitable measure as the informativeness indicator to improve the ranking adjustment strategy.

https://doi.org/ 10.1155/2017/5208072

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Acknowledgments

This work was supported by the National Key Technology R&D Program (No. 2015BAH14F02) and Project 61325008 (Mining and Management of Large Scale Process Data) which is supported by the NSFC.

References

[1] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent Dirichlet allocation," The Journal of Machine Learning Research, vol. 3, no. 1, pp. 993-1022, 2003.

[2] Z. Huang, X. Lu, and H. Duan, "Latent treatment pattern discovery for clinical processes," Journal of Medical Systems, vol. 37, no. 2, pp. 1-10, 2013.

[3] Z. Huang, W. Dong, L. Ji, C. Gan, X. Lu, and H. Duan, "Discovery of clinical pathway patterns from event logs using probabilistic topic models," Journal of Biomedical Informatics, vol. 47, no. 1, pp. 39-57, 2014.

[4] Z. Huang, W. Dong, P. Bath, L. Ji, and H. Duan, "On mining latent treatment patterns from electronic medical records," Data Mining and Knowledge Discovery, vol. 40, no. 1, pp. 1-36, 2014.

[5] Z. Huang, W. Dong, L. Ji, C. He, and H. Duan, "Incorporating comorbidities into latent treatment pattern mining for clinical pathways," Journal of Biomedical Informatics, vol. 59, no. 1, pp. 227-239, 2016.

[6] X. Xu, T. Jin, Z. Wei, C. Lv, and J. Wang, "TCPM: topic-based clinical pathway mining," in 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 292-301, IEEE, Washington, DC, USA, June 2016.

[7] F.-r. Lin, S.-c. Chou, S.-m. Pan, and Y.-m. Chen, "Mining time dependency patterns in clinical pathways," International Journal of Medical Informatics, vol. 62, no. 1, pp. 11-25, 2001.

[8] F.-r. Lin, L.-s. Hsieh, and S.-m. Pan, "Learning clinical pathway patterns by hidden Markov model," in Proceedings of the 38th Annual Hawaii International Conference on System Sciences, 2005 (HICSS'05), IEEE, p. 142a, January 2005.

[9] R. S. Mans, M. Schonenberg, M. Song, W. M. van der Aalst, and P. J. Bakker, "Application of Process Mining in Healthcare--A Case Study in a Dutch Hospital," Biomedical Engineering Systems and Technologies, vol. 25, no. 1, pp. 425-438, 2009.

[10] A. Weijters and W. M. Van der Aalst, "Rediscovering workflow models from event-based data using little thumb," Integrated Computer-Aided Engineering, vol. 10, no. 2, pp. 151-162, 2003.

[11] M. Lang, T. Burkle, S. Laumann, and H.-U. Prokosch, "Process mining for clinical workflows: challenges and current limitations," in E-Health Beyond the Horizon: Get IT There: Proceedings of MIE2008, the XXIst International Congress of the European Federation for Medical Informatics, IOS Press, p. 229, 2008.

[12] J. Ghattas, M. Peleg, P. Soffer, and Y. Denekamp, "Learning the context of a clinical process," in Business Process Management Workshops, pp. 545-556, Springer, Berlin Heidelberg, 2009.

[13] A. Rebuge and D. R. Ferreira, "Business process analysis in healthcare environments: a methodology based on process mining," Information Systems, vol. 37, no. 2, pp. 99-116, 2012.

[14] Y. Zhang, R. Padman, and N. Patel, "Paving the cowpath: learning and visualizing clinical pathways from electronic health record data," Journal of Biomedical Informatics, vol. 58 no. 1, pp. 186-197, 2015.

[15] G. T. Lakshmanan, S. Rozsnyai, and F. Wang, "Investigating clinical care pathways correlated with outcomes," in Business Process Management, pp. 323-338, Springer, Berlin Heidelberg, 2013.

[16] Z. Huang, X. Lu, H. Duan, and W. Fan, "Summarizing clinical pathways from event logs," Journal of Biomedical Informatics, vol. 46, no. 1, pp. 111-127, 2013.

[17] A. Perer, F. Wang, and J. Hu, "Mining and exploring care pathways from electronic medical records with visual analytics," Journal of Biomedical Informatics, vol. 56 no. 1, pp. 369-378, 2015.

[18] U. Kaymak, R. Mans, T. van de Steeg, and M. Dierks, "On process mining in health care," in 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1859-1864, IEEE, South Korea, October 2012.

[19] C. Fernandez-Llatas, A. Martinez-Millana, A. Martinez-Romero, J. M. Benedi, and V. Traver, "Diabetes care related process modelling using process mining techniques. Lessons learned in the application of interactive pattern recognition: coping with the spaghetti effect," in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 2127-2130, IEEE, Italy, August 2015.

[20] A. Dagliati, L. Sacchi, C. Cerra et al., "Temporal data mining and process mining techniques to identify cardiovascular risk-associated clinical pathways in type 2 diabetes patients," in 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 240-243, IEEE, Spain, June 2014.

[21] X. Xu, T. Jin, and J. Wang, "Summarizing patient daily activities for clinical pathway mining," in 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1-6, IEEE, Germany, September 2016.

[22] J. Chang, S. Gerrish, C. Wang, J. L. Boyd-Graber, and D. M. Blei, "Reading tea leaves: how humans interpret topic models," Advances in Neural Information Processing Systems, pp. 288-296, 2009.

[23] M. Danilevsky, C. Wang, N. Desai, X. Ren, J. Guo, and J. Han, "Automatic construction and ranking of topical keyphrases on collections of short documents," in Proceedings of the 2014 SIAM International Conference on Data Mining (SDM'14), pp. 398-406, Society for Industrial and Applied Mathematics, 2014.

Xiao Xu, Tao Jin, Zhijie Wei, and Jianmin Wang

School of Software, Tsinghua University, Beijing, China

Correspondence should be addressed to Tao Jin; jintao05@gmail.com

Received 4 November 2016; Revised 24 January 2017; Accepted 5 February 2017; Published 15 May 2017

Academic Editor: Evangelos Sakkopoulos

Caption: FIGURE 1: Two problems in applying LDA on clinical data: (a) the clinical activities in one day for a patient and (b) the three discovered topics.

Caption: FIGURE 2: The illustrative process of our approach. The top part is the pipeline, middle part is the business domain, and bottom part is the corresponding algorithm domain (T: patient trace; a: clinical activity; day: clinical day; G: group; VOC: vocabulary).

Caption: Figure 3: Graphical representation of (a) LDA and (b) CDG-LDA.

Caption: FIGURE 4: Redundancy indicator RE on various topic number K and top size N.

Caption: FIGURE 5: ICH process model.

Caption: FIGURE 6: IH process model.
TABLE 1: The national clinical pathway of intracerebral hemorrhage
released by the Ministry of Health of China.

Stage         Order

Stage 1       Long-term medical order:
(Day 1)
              (1) neurology nursing
              routine, (2) level I
              care, (3) normal diet,
              (4) keep the bed, and
              (5) observing vital
              signs

              Temporary medical order:

              (1) blood, urine, and
              stool routine
              examination; (2)
              hepatorenal function,
              electrolytes, blood
              glucose, blood lipids,
              cardiac enzymes,
              coagulation function,
              and infectious disease
              screening; (3) brain CT,
              chest X-ray, and ECG;
              and (4) when necessary:
              brain MRI, CTA, MRA, or
              DSA

Stage 2       Long-term medical order:
(Day 2)
              (1) neurology nursing
              routine, (2) level I
              care, (3) normal diet,
              (4) keep the bed, (5)
              observing vital signs,
              and (6) basic drugs

              Temporary medical order:

              (1) re-examination for
              abnormal laboratory
              values and (2) when
              necessary: re-
              examination CT

Stage 3       Long-term medical order:
(Day 3)
              (1) neurology nursing
              routine, (2) level I
              care, (3) normal diet,
              (4) keep the bed, (5)
              observing vital signs,
              and (6) basic drugs

              Temporary medical order:

              (1) re-examination for
              abnormal laboratory
              values

Stage 4       Long-term medical order:
(Days 4-6)
              (1) neurology nursing
              routine, (2) level II
              care, (3) normal diet,
              (4) keep the bed, (5)
              observing vital signs,
              and (6) basic drugs

              Temporary medical order:

              (1)    re-examination
              for abnormal laboratory
              values; (2)    re-
              examination for blood,
              kidney function, blood
              glucose, and
              electrolytes; and (3)
              when necessary: re-
              examination CT

Stage 5       Long-term medical order:
(Days 7-13)
              (1) neurology nursing
              routine, (2) level II-III
              care, (3) normal
              diet, (4) observing
              vital signs, and (5)
              basic drugs

              Temporary medical order:

              (1) re-examination for
              abnormal laboratory
              values and (2) when
              necessary: DSA, CTA, and
              MRA

Stage 6       Long-term medical order:
(Days 8-14)
              (1) discharge with drugs

TABLE 2: Meanings of the notations.

Notation                           Meaning

D                          The set of clinical days

A                            The set of clinical
                                  activities

Z                             The set of topics
                                assigned to A

D,K                         The number of clinical
                               days and topics

V                            The number of unique
                             clinical activities

[N.sub.d]                   The number of clinical
                              activities in dth
                                 clinical day

[N.sub.k]                   The number of clinical
                             activities that are
                               assigned topic k

[G.sub.d]                    The number of groups
                               (unique clinical
                              activities) in dth
                                 clinical day

[A.sup.g.sub.d]             The number of clinical
                           activities in gth group
                             in dth clinical day

[a.sub.d,i]                    The ith clinical
                              activities in dth
                                 clinical day

[z.sub.d,i]                     The topic for
                                 [a.sub.d,i]

[a.sup.g.sub.d]            The clinical activities
                             of gth group in dth
                                 clinical day

[a.sup.g.sub.d,j]              The jth clinical
                           activity in gth group in
                               dth clinical day

[z.sup.g.sub.d,j]               The topic for
                              [a.sup.g.sub.d,j]

[z.sup.g.sub.d]            [mathematical expression
                            not reproducible] the
                            set of topics for gth
                            group in dth clinical
                                     day

[N.sup.(k).sub.d]           The number of clinical
                             activities that are
                           assigned topic k in dth
                                 clinical day

[N.sup.(v).sub.k]           The number of clinical
                           activities with value v
                                 and topic k

[mathematical expression    The number of clinical
not reproducible]            activities that are
                           assigned topic k in dth
                           clinical day, except gth
                            group in dth clinical
                                     day

[mathematical expression    The number of clinical
not reproducible]          activities with value v
                           and topic k, except gth
                            group in dth clinical
                                     day

[alpha],[beta]              Dirichlet prior vector

[phi],[theta]              Multinomial distribution
                           over clinical activities
                                  and topics

[[phi].sub.k]              Multinomial distribution
                           over clinical activities
                                 for topic k

[[theta].sub.d]            Multinomial distribution
                             over topics for dth
                                 clinical day

TABLE 3: Statistics of our datasets.

Disease   Trace    Day      Voc      Avg   Min   Max
          number   number   number   LOS   LOS   LOS

ICH       240      3204     752      14    2     34
IH        33       241      447      6     2     10

TABLE 4: Eight labeled topics of ICH by different methods.

Number        Topic tag                   LDA

1              Imaging                (1) Doppler
                                 echocardiography, (2)
                                Doppler ultrasonography
                                  of left ventricular
                               function, (3) ventricular
                                   wall motion, (4)
                               transcranial Doppler, (5)
                                analysis of ultrasonic,
                                   (6) color Doppler
                                  ultrasonography of
                                     abdomen, (7)
                                  X-radiography, (8)
                                digitized photography,
                                (9) B mode ultrasound,
                                     and (10) film

2            Medication          (1) Aceglutamide, (2)
                                 sodium chloride, (3)
                                  local infiltration
                               anesthesia, (4) etimicin,
                               (5) cerebrosidekinin, (6)
                                 physical cooling, (7)
                                  t-branch pipe, (8)
                                aerosol inhalation, (9)
                               ambroxol, and (10) venous
                                      transfusion

3            High-level        (1) Level I care, (2) ECG
            nursing care         monitoring, (3) blood
                                   oxygen saturation
                                   monitoring, (4)
                               urinary meatus care, (5)
                                 mouth care, (6) skin
                                  care, (7) hospital
                               examining fee, (8) mouth
                                   care package, (9)
                               ambulatory blood pressure
                                 monitoring, and (10)
                                   continuous oxygen
                                      inhalation

4          Regular nursing      (1) Level II care, (2)
         care and medication    hospital examining fee,
                                 (3) pantoprazole, (4)
                               vitamin B6, (5) mannitol,
                                  (6) vitamin C, (7)
                                  potassium magnesium
                                 aspartate, (8) venous
                                transfusion, (9) sodium
                                  chloride, and (10)
                                   glycerin fructose

5             Admission         (1) ECG, (2) PLGA, (3)
             examination         fibrous protein, (4)
                                blood collection tube,
                                   (5) hemostix, (6)
                               washbasin, (7) ECG event
                                    log, (8) plasma
                                prothrombin time assay,
                                 (9) activated partial
                               thromboplastin time, and
                                 (10) prothrombin time
                                         assay

6            Biochemical          (1) Urea assay, (2)
                exam             creatinine assay, (3)
                                 uric acid assay, (4)
                               chloride assay, (5) blood
                                  cell analysis, (6)
                                 potassium assay, (7)
                               sodium assay, (8) glucose
                                   assay, (9) serum
                                bicarbonate assay, and
                                    (10) excrement

7            Biochemical       (1) Serum a-L-glucosidase
                exam              assay, (2) serum 5'
                                nucleotidase assay, (3)
                                plasma viscosity assay,
                                   (4) glycosylated
                                 hemoglobin assay, (5)
                                whole blood viscosity,
                               (6) identification of Rh
                               blood group antigen, (7)
                               amylase assay, (8) serum
                                  creatine kinase-MB
                               isoenzyme activity assay,
                               (9) lactate dehydrogenase
                                 assay, and (10) serum
                               alanine aminotransferase
                                         assay

8            Biochemical          (1) Serum aspartate
                exam            aminotransferase assay,
                                  (2) electrophoresis
                                  analysis of lactate
                               dehydrogenase isoenzymes,
                                (3) urine analysis, (4)
                                  serum 7 pancreatic
                                acyltransferase assay,
                                  (5) serum alkaline
                                phosphatase assay, (6)
                                     serum alanine
                                aminotransferase assay,
                                  (7) urinalysis, (8)
                                 hepatitis A antibody
                                   assay, (9) serum
                               high-density lipoprotein
                                cholesterol assay, and
                                   (10) serum total
                                   cholesterol assay

Number    Topic tag              CDG-LDA

1          Imaging             (1) Doppler
                         echocardiography, (2)
                         Doppler ultrasonography
                           of left ventricular
                        function, (3) ventricular
                            wall motion, (4)
                        transcranial Doppler, (5)
                         analysis of ultrasonic,
                            (6) color Doppler
                         ultrasonography of the
                             abdomen, (7) X-
                         radiography, (8) B mode
                        ultrasound, (9) digitized
                          photography, and (10)
                                  film

2         Medication      (1) Pantoprazole, (2)
                           potassium magnesium
                        aspartate, (3) mannitol,
                           (4) vitamin B6, (5)
                         glycerin fructose, (6)
                        vitamin C, (7) naloxone,
                        (8) piracetam, (9) sodium
                           chloride, and (10)
                                 glucose

3         High-level    (1) Level I care, (2) ECG
         nursing care     monitoring, (3) blood
                            oxygen saturation
                          monitoring, (4) mouth
                        care, (5) urinary meatus
                        care, (6) skin care, (7)
                            continuous oxygen
                          inhalation, (8) mouth
                            care package, (9)
                        ambulatory blood pressure
                          monitoring, and (10)
                              drainage pack

4          Regular       (1) Level II care, (2)
         nursing care    venous transfusion, (3)
                        flusher, (4) syringe, (5)
                        arterial/venous cannula,
                        (6) therapy application,
                          (7) aceglutamide, (8)
                        intramuscular injection,
                          (9) physical cooling
                         venipuncture, and (10)
                               nifedipine

5         Admission     (1) Brain CT, (2) venous
         examination     sampling, (3) ECG, (4)
                        hemostix, (5) electrode,
                         (6) 12 channel dynamic
                         electrocardiogram, (7)
                        washbasin, (8) ECG event
                        log, (9) nasogastric, and
                           (10) evaluation of
                           activities of daily
                                 living

6        Biochemical    (1) Creatinine assay, (2)
             exam       urea assay, (3) uric acid
                          assay, (4) blood cell
                         analysis, (5) chloride
                        assay, (6) sodium assay,
                        (7) potassium assay, (8)
                        serum bicarbonate assay,
                         (9) glucose assay, and
                           (10) calcium assay

7        Biochemical    (1) Serum 5' nucleotidase
             exam         assay, (2) serum a-L-
                         glucosidase assay, (3)
                         plasma viscosity assay,
                           (4) serum creatine
                           kinase-MB isoenzyme
                           activity assay, (5)
                         glycosylated hemoglobin
                         assay, (6) whole blood
                             viscosity, (7)
                          identification of Rh
                        blood group antigen, (8)
                           amylase assay, (9)
                          lactate dehydrogenase
                          assay, and (10) serum
                           total protein assay

8        Biochemical       (1) Serum aspartate
             exam        aminotransferase assay,
                            (2) serum albumin
                        assay, (3) urine sediment
                        quantitative analyze, (4)
                          inorganic phosphorus
                               assay, (5)
                        electrophoresis analysis
                        of lactate dehydrogenase
                          isoenzymes, (6) serum
                        fructose amine assay, (7)
                        urine analysis, (8) serum
                              7 pancreatic
                         acyltransferase assay,
                            (9) serum alanine
                         aminotransferase assay,
                         and (10) serum alkaline
                            phosphatase assay

TABLE 5: Five labeled topics of IH by different methods.

Number    Topic tag                LDA

1        Biochemical    (1) Blood cell analysis,
             exam        (2) glucose assay, (3)
                          creatinine assay, (4)
                        urea assay, (5) chloride
                        assay, (6) sodium assay,
                        (7) potassium assay, (8)
                          uric acid assay, (9)
                         calcium assay, and (10)
                             magnesium assay

2         Infectious    (1) Hepatitis A antibody
         disease exam     assay, (2) treponema
                        pallidum antibody assay,
                         (3) HIV antibody assay,
                          (4) hepatitis B core
                           antibody assay, (5)
                          hepatitis Be antibody
                         assay, (6) hepatitis C
                           antibody assay, (7)
                           hepatitis B surface
                           antibody assay, (8)
                           hepatitis B surface
                        antigen assay, (9) urine
                           analysis, and (10)
                            urinary sediment
                               microscopy

3         Admission         (1) ECG, (2) ECG
             exam          monitoring, (3) ABO
                        subtype assay, (4) urine
                        routines, (5) prothrombin
                        time assay, (6) activated
                         partial thromboplastin
                            time, (7) plasma
                         prothrombin time assay,
                          (8) washbasin, (9) Rh
                         subtype assay, and (10)
                              color Doppler
                         ultrasonography of the
                                 abdomen

4          Regular       (1) Level II care, (2)
         nursing care    hospital examining fee,
                         (3) level III care, (4)
                         infusion apparatus, (5)
                         venous transfusion, (6)
                         hemostix, (7) syringe,
                        (8) special hemostix, (9)
                        dressing change, and (10)
                         arterial/venous cannula

5          Surgery       (1) Level II care, (2)
         and regular      endotherm knife, (3)
         nursing care       mask, (4) venous
                        transfusion, (5) hospital
                           examining fee, (6)
                        glucose, (7) syringe, (8)
                        ECG monitoring, (9) blood
                         oxygen saturation, and
                          (10) sodium chloride

Number    Topic tag              CDG-LDA

1        Biochemical    (1) Blood cell analysis,
             exam        (2) glucose assay, (3)
                            sodium assay, (4)
                           chloride assay, (5)
                        potassium assay, (6) uric
                             acid assay, (7)
                          creatinine assay, (8)
                         urea assay, (9) calcium
                        assay, and (10) magnesium
                                  assay

2        Presurgical      (1) Skin preparation
         preparation        package, (2) skin
                            preparation, (3)
                          disinfection fee, (4)
                         hygienic material, (5)
                             washbasin, (6)
                        intramuscular injection,
                        (7) health education, (8)
                           BP monitoring, (9)
                          cefuroxime, and (10)
                           infusion apparatus

3         Admission         (1) ECG, (2) ECG
             exam         monitoring, (3) urine
                          routines, (4) urinary
                        sediment microscopy, (5)
                           ECG event log, (6)
                        abdomen X-ray, (7) chest
                        X-ray, (8) color Doppler
                           ultrasonography of
                         superficial organ, (9)
                              color Doppler
                           ultrasonography of
                            abdomen, and (10)
                         analysis of ultrasonic

4          Regular       (1) Level II care, (2)
         nursing care      level III care, (3)
                        dressing change, (4) fat/
                          soluble vitamin, (5)
                         cefotiam, (6) arterial/
                           venous cannula, (7)
                         venous transfusion, (8)
                         small dressing change,
                          (9) levofloxacin, and
                          (10) middle dressing
                                 change

5          Surgery       (1) Mask, (2) endotherm
                           knife, (3) surgery
                        package, (4) blood oxygen
                         saturation, (5) lumbar
                        anesthesia, (6) epidural
                             anesthesia, (7)
                         application, (8) oxygen
                         therapy, (9) IH repair,
                           and (10) anesthesia
                               monitoring

TABLE 6: Topic coherence in terms of NKQM@N.

Dataset   Method    NKQM@5   NKQM@10   NKQM@20

ICH         LDA     0.8211   0.8004    0.7907
          CDG-LDA   0.8448   0.8498    0.8235
IH          LDA     0.7915    0.783    0.7631
          CDG-LDA   0.8316   0.8266    0.8116
COPYRIGHT 2017 Hindawi Limited
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2017 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research Article
Author:Xu, Xiao; Jin, Tao; Wei, Zhijie; Wang, Jianmin
Publication:Journal of Healthcare Engineering
Date:Jan 1, 2017
Words:8579
Previous Article:A 3D Scan Model and Thermal Image Data Fusion Algorithms for 3D Thermography in Medicine.
Next Article:Machine Learning Theory and Applications for Healthcare.
Topics:

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters