Semantic Web for Effective Healthcare Systems. Группа авторов

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СКАЧАТЬ 776 Infrastructure 554 Time 704

Number of reviews
H1	596	H6	308
H2	411	H7	313
H3	399	H8	297
H4	227	H9	281
H5	252	H10	361

      Table 1.3 shows that 663 reviews were collected for the feature “cost,” and 596 reviews were collected for the hospital H1. The list of features is identified from the previous work, related articles, and social media websites. For example, the document [62] clearly identified the necessary criteria for healthcare services. The expectations of patients such as cost, hospital characteristics, infrastructure facility, recommendations by other users, treatment and nursing care, and medical expertise were clearly mentioned in the article [63]. Features were also selected by referring web sites like, BestHospitalAdvisor, Mouthshut.com, webometrics, and so on. The comments given by patients or by their care takers in these websites were analyzed for different features of healthcare services of these different hospitals.

1.7 Results and Discussions

Ontology-based Semantic Indexing (OnSI) model builds domain Ontology for each product/service review documents using the selected features from the CFS_LDA model. Protégé software is used to build and query the Ontology model. The top five terms selected by LDA model, related to each topic for the dataset is shown in Table 1.4. Each topic is manually labeled in context with the first term in the list. It is difficult to carry out human annotation for all the terms grouped under the topic.

Table 1.4 List of top five terms by LDA model.

Topics/features of (DS1)	Feature terms by FSLDA model (DS1)
Topic 1—Cost	cost, test, money, charge, day
Topic 2—Medicare	doctor, nurse, team, treatment, bill
Topic 3—Staff	staff, patient, child, problem, face
Topic 4—Infrastructure	hospital, people, room, experience, surgery
Topic 5—Time	time, operation, hour, service, check

For example, the term “bill” is one of the top words under the topic “medicare.” However, it is more related to the topics “cost” or “infrastructure” or “time.” Similarly, the term “appointment” is not present in any of the list under top 5 or top 10 terms; however, it is more appropriate to the topics “time” and “medicare.” In order to alleviate this problem, the CFS_LDA model selects the representative terms of each topic with reference to the first term (the term which has the highest term-topic probability in each topic) in the list, using the correlation analysis. As stated in the previous example, the term “bill” is not related to the term “doctor,” and it is highly correlated with the terms “cost,” “hospital,” and “time.” The correlation values of these terms are shown in Table 1.5. For example, the term-topic probability “Φ_tw” of “room” is 0.0134 and correlated value “c” with “cost” is 0.0222. As stated in another example, the term “appointment” is highly correlated with the terms “doctor” and “time,” and it is grouped under the topic “medicare” and “time” as shown in Table 1.5. As an another example, the term “disease” is related with “doctor” and “hospital,” and it is not related with the terms “cost,” “time,” and “staff,” as shown in Table 1.5.

Table 1.5 Sample correlated terms selected by CFS_LDA.

Features		Cost	Medicare	Staff	Infrastructure	Time
High probable terms		cost	doctor	Staff	Hospital	time
Term-topic probability (Φtw)		0.0923	0.2132	0.2488	0.3152	0.1247
Correlated value (c )		1	1	1	1	1
Sample terms modeled by CFSLDA
Room	Φtw C	0.01340.0222	0.00040.1378	0.00050.2392	0.04710.0402	0.01340.0222
Disease	Φtw C	0.0004-0.0347	0.02220.0547	0.0005-0.0462	0.00040.0948	0.0004+0.0408
Appointment	Φtw C	0.0134-0.0343	0.00920.1802	0.0005-0.0462	0.0004-0.0414	0.00040.0477
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