title: Use of a Latent Topic Model for Characteristic Extraction from Health Checkup Questionnaire Data.
abstract: OBJECTIVE
  When patients complete questionnaires during health checkups, many of their responses are subjective, making topic extraction difficult. Therefore, the purpose of this study was to develop a model capable of extracting appropriate topics from subjective data in questionnaires conducted during health checkups.
  
  
  METHODS
  We employed a latent topic model to group the lifestyle habits of the study participants and represented their responses to items on health checkup questionnaires as a probability model. For the probability model, we used latent Dirichlet allocation to extract 30 topics from the questionnaires. According to the model parameters, a total of 4381 study participants were then divided into groups based on these topics. Results from laboratory tests, including blood glucose level, triglycerides, and estimated glomerular filtration rate, were compared between each group, and these results were then compared with those obtained by hierarchical clustering.
  
  
  RESULTS
  If a significant (p < 0.05) difference was observed in any of the laboratory measurements between groups, it was considered to indicate a questionnaire response pattern corresponding to the value of the test result. A comparison between the latent topic model and hierarchical clustering grouping revealed that, in the latent topic model method, a small group of participants who reported having subjective signs of urinary disorder were allocated to a single group.
  
  
  CONCLUSIONS
  The latent topic model is useful for extracting characteristics from a small number of groups from questionnaires with a large number of items. These results show that, in addition to chief complaints and history of past illness, questionnaire data obtained during medical checkups can serve as useful judgment criteria for assessing the conditions of patients.
authors.affiliation: Yutaka Hatakeyama, Center of Medical Information Science, Kochi University Medical School, Oko-cho Kohasu, Nankoku, Kochi 783-8505, Japan, E-mail: hatake@kochi-u.ac.jp.
authors.forename: Y
authors.initials: Y
authors.lastname: Hatakeyama
authors.forename: I
authors.initials: I
authors.lastname: Miyano
authors.forename: H
authors.initials: H
authors.lastname: Kataoka
authors.forename: N
authors.initials: N
authors.lastname: Nakajima
authors.forename: T
authors.initials: T
authors.lastname: Watabe
authors.forename: N
authors.initials: N
authors.lastname: Yasuda
authors.forename: Y
authors.initials: Y
authors.lastname: Okuhara
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006304:Health Status; D006801:Humans; D007564:Japan; D008019:Life Style; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D010808:Physical Examination / Q000706:statistics & numerical data*; D018401:Sample Size; D057566:Self Report
keywords: Health status; classification; health checkup questionnaire; latent Dirichlet allocation