[1N3-GS-10] AI application

[1N3-GS-10-03] Consideration of Applicability of Causal Discovery methods to Health Checkup data

〇Satoki Fujita¹, Yuki Yoshida¹, Yoshitake Kitanishi¹ (1. Shionogi & Co. Ltd.)

Keywords:Causal discovery, Health checkup data

In terms of health management for employees, it is necessary to implement appropriate preventive measures against disorders that significantly affect presenteeism such as depression. However, although these disorders are known to be closely related to daily lifestyle, the causal structure itself is still unclear. In order to solve this problem, health checkup data can be used to clarify the dependence of each lifestyle factor in a data-driven manner, which may lead to the examination of effective measures. As a preliminary study for the above purpose, we focus on causal search methods (Bayesian Network, LiNGAM). Through the results of application to the health checkup database, we compared and examined the characteristics and usefulness of these methods from a practical point of view. It was confirmed that these methods can help achieve the above purposes if we recognize the shortcomings of the methods and do not misuse them.