Deep Learning for EHR-Based Diagnosis Prediction: A General Recipe Open Access

Abstract

With the rapid accumulation of Electronic Health Records (EHRs) and the recent advance in data-driven algorithms, deep learning models have been increasingly applied to tasks in EHR-based predictive healthcare. This paper, motivated by the hierarchical structure of EHR data and the identified challenges in predictive healthcare, mathematically formulates a general architecture for learning patient representations for diagnosis prediction. With the guidance of the proposed general architecture, this work further discusses how existing works have incorporated various model designs to overcome certain challenges from four levels: diagnosis, visit, sequence, and framework-level. Through these discussions, this paper serves as a summary of existing works in modeling sequential EHR data, a cookbook for novices interested in EHR-based predictive healthcare, and a foundation for a future work, the idea of which are also introduced in this paper.

Table of Contents

Introduction 1

General Recipe 6

Diagnosis-Level Representation Learning 9

Visit-Level Representation Learning 13

Sequence-Level Representation Learning 16

Framework-Level Design 20

Conclusion 22

Future Work 23

About this Honors Thesis

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School	Emory College
Department	Mathematics
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Health Sciences, Health Care Management Computer Science Applied Mathematics
Keyword	Electronic Health Records Predictive Healthcare Data Mining Machine Learning
Committee Chair / Thesis Advisor	Yang, Carl, Emory University
Committee Members	Ettinger, Bree, Emory University Fossati, Davide, Emory University Zureick-Brown, David, Emory University

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