Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study.

ABSTRACT:

Background

Coronary heart disease (CHD) has become the leading cause of death and one of the most serious epidemic diseases worldwide. CHD is characterized by urgency, danger and severity, and dynamic treatment strategies for CHD patients are needed. We aimed to build and validate an AI model for dynamic treatment recommendations for CHD patients with the goal of improving patient outcomes and learning best practices from clinicians to help clinical decision support for treating CHD patients.

Methods

We formed the treatment strategy as a sequential decision problem, and applied an AI supervised reinforcement learning-long short-term memory (SRL-LSTM) framework that combined supervised learning (SL) and reinforcement learning (RL) with an LSTM network to track patients' state

SUBMITTER: Guo H 

PROVIDER: S-EPMC8845235 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature