Department of Information and Industrial Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Department of Internal Medicine, Seoul Red Cross Hospital, Seoul, 03181, Republic of Korea.
Sci Rep. 2021 Mar 25;11(1):6920. doi: 10.1038/s41598-021-86419-4.
The extensive utilization of electronic health records (EHRs) and the growth of enormous open biomedical datasets has readied the area for applications of computational and machine learning techniques to reveal fundamental patterns. This study's goal is to develop a medical treatment recommendation system using Korean EHRs along with the Markov decision process (MDP). The sharing of EHRs by the National Health Insurance Sharing Service (NHISS) of Korea has made it possible to analyze Koreans' medical data which include treatments, prescriptions, and medical check-up. After considering the merits and effectiveness of such data, we analyzed patients' medical information and recommended optimal pharmaceutical prescriptions for diabetes, which is known to be the most burdensome disease for Koreans. We also proposed an MDP-based treatment recommendation system for diabetic patients to help doctors when prescribing diabetes medications. To build the model, we used the 11-year Korean NHISS database. To overcome the challenge of designing an MDP model, we carefully designed the states, actions, reward functions, and transition probability matrices, which were chosen to balance the tradeoffs between reality and the curse of dimensionality issues.
电子健康记录（EHRs）的广泛应用和大量开放的生物医学数据集的增长，为应用计算和机器学习技术来揭示基本模式做好了准备。本研究的目的是使用韩国的电子健康记录和马尔可夫决策过程（MDP）开发一种治疗推荐系统。韩国国家健康保险共享服务（NHISS）共享 EHRs，使得分析包括治疗、处方和体检在内的韩国人的医疗数据成为可能。在考虑了这些数据的优点和有效性之后，我们分析了患者的医疗信息，并为糖尿病患者推荐了最佳的药物处方，因为糖尿病是韩国人最负担不起的疾病。我们还为糖尿病患者提出了一种基于 MDP 的治疗推荐系统，以帮助医生开具糖尿病药物。为了构建模型，我们使用了 11 年的韩国 NHISS 数据库。为了克服设计 MDP 模型的挑战，我们仔细设计了状态、动作、奖励函数和转移概率矩阵，这些设计旨在平衡现实和维度问题的诅咒之间的权衡。
