Abstract

Objective: Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seeking second opinions by training two agents with different focuses: Primary agent studies the most recent visit of the patient to learn the current health status. Then second-opinion agent considers the entire patient history to obtain a more global view.
Materials and Methods: Our approach Dr. Agent augments recurrent neural networks with two policy gradient agents. Moreover, Dr. Agent is customized with various patient demographics information and learns a dynamic skip-connection to focus on the relevant information over time. We trained Dr. Agent to perform 4 clinical prediction tasks on the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database: (1) In-hospital mortality prediction, (2) Acute care phenotype classification, (3) Physiologic Decompensation prediction, (4) Forecasting length of stay. We compared the performance of Dr. Agent against four baseline clinical predictive models.
Results: Dr. Agent outperforms baseline clinical prediction models across all 4 tasks in terms of all metrics. Compared with the best baseline model, Dr. Agent achieves up to 15% higher area under the precision recall curve (AUPRC) on different tasks.
Conclusions: Dr. Agent can comprehensively model the long-term dependencies of patients' health status while considering patients' demographics using two agents, and therefore achieves better prediction performance on different clinical prediction tasks.