Using artificial intelligence to learn optimal regimen plan for Alzheimer's disease.

ABSTRACT:

Background

Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population.

Objective

Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude data from electronic health records.

Methods

In this study, we selected 1736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases-depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). We modeled th

SUBMITTER: Bhattarai K 

PROVIDER: S-EPMC10531148 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature