Deep Learning and Alternative Learning Strategies for Retrospective Real-world Clinical Data

Overview

Journal NPJ Digit Med

Specialty Medical Informatics

Date 2019 Jul 16

PMID 31304389

Citations 77

Authors

David Chen

Sijia Liu

Paul Kingsbury

Sunghwan Sohn

Curtis B Storlie

Elizabeth B Habermann

James M Naessens

David W Larson

Hongfang Liu

Affiliations

Soon will be listed here.

Abstract

In recent years, there is increasing enthusiasm in the healthcare research community for artificial intelligence to provide big data analytics and augment decision making. One of the prime reasons for this is the enormous impact of deep learning for utilization of complex healthcare big data. Although deep learning is a powerful analytic tool for the complex data contained in electronic health records (EHRs), there are also limitations which can make the choice of deep learning inferior in some healthcare applications. In this paper, we give a brief overview of the limitations of deep learning illustrated through case studies done over the years aiming to promote the consideration of alternative analytic strategies for healthcare.

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