Construction of Knowledge Learning Model for Tuberculosis Contact Investigation Using Bayesian Network

2019 Fiscal Year Final Research Report

• Project/Area Number: 17K09222
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Hygiene and public health
• Research Institution: 公益財団法人結核予防会 結核研究所
• Principal Investigator: Uchimura Kazuhiro 公益財団法人結核予防会 結核研究所, 臨床・疫学部 疫学情報室, 副部長 (30247283)
• Co-Investigator(Kenkyū-buntansha): 河津 里沙 公益財団法人結核予防会 結核研究所, 臨床・疫学部, 研究員 (10747570)
• Project Period (FY): 2017-04-01 – 2020-03-31
• Keywords: 結核 / 接触者健診 / ベイジアンネットワーク

Outline of Final Research Achievements

A Tuberculosis contact investigation model based on the characteristics of the index tuberculosis case and contacts was constructed using the Bayesian network method. And the probability of infection of IGRA-positive people was estimated by learning from contact investigation result. Regarding the attribute information, the infection probability (true infection probability) of the IGRA-positive patients when the information was used as a reference and when the information was learned was estimated and compared. The items with high information value as learning were items of initial patient cavity information, contact type, contact density, and contact items for foreign births. The simulation data used include outbreak cases, and the infection rate of the contact target population is considered to be relatively high. However, the estimated a priori probability of infection is about 1.2 to 1.7 times the prior probability.

Free Research Field

結核疫学

Academic Significance and Societal Importance of the Research Achievements

一部実データには情報なしの項目等があるため、現時点では補完したシミュレーションデータによる解析でモデル精度向上をすすめているが、実情報量が充足するにつれ、より現実的分析に近づくと思われる。また。諸項目の入力後(あり/なし)のIGRA陽性確率(事後分布として)の推定も接触者健診実施上の有効情報になると思われる。