Deciphering Clinical Narratives - Augmented Intelligence for Decision Making in Healthcare Sector

Abstract

Clinical notes that describe details about diseases, symptoms, treatments, and observed reactions of patients to them, are valuable resources to generate insights about the effectiveness of treatments. Their role in designing better clinical decision making systems is being increasingly acknowledged. However, the availability of clinical notes is still an issue due to privacy violation concerns. Hence most of the work done are on small datasets and neither the power of machine learning is fully utilized, nor is it possible to validate the models properly. With the availability of the Medical Information Mart for Intensive Care (MIMIC-III v1.4) dataset for researchers though, the problem has been somewhat eased. In this paper we have presented an overview of our earlier work on designing deep neural models for prediction of outcomes and hospital stay for patients using MIMIC data. We have also presented new work on patient stratification and explanation generation for patient cohorts. This is early work targeted towards studying trajectories for treatment for different cohorts of patients, which can ultimately lead to discovery of low-risk models for individual patients to ensure better outcomes.