Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records

by   Eunbyeol Cho, et al.

Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain. Recent work presented a promising framework that embeds entire features in raw EHR data regardless of its form and medical code standards. The framework, however, only focuses on encoding EHR with minimal preprocessing and fails to consider how to learn efficient EHR representation in terms of computation and memory usage. In this paper, we search for a versatile encoder not only reducing the large data into a manageable size but also well preserving the core information of patients to perform diverse clinical tasks. We found that hierarchically structured Convolutional Neural Network (CNN) often outperforms the state-of-the-art model on diverse tasks such as reconstruction, prediction, and generation, even with fewer parameters and less training time. Moreover, it turns out that making use of the inherent hierarchy of EHR data can boost the performance of any kind of backbone models and clinical tasks performed. Through extensive experiments, we present concrete evidence to generalize our research findings into real-world practice. We give a clear guideline on building the encoder based on the research findings captured while exploring numerous settings.


page 18

page 19

page 24

page 25


Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Automatically extracting useful information from electronic medical reco...

Embedding Complexity In the Data Representation Instead of In the Model: A Case Study Using Heterogeneous Medical Data

Electronic Health Records have become popular sources of data for second...

Toward Cohort Intelligence: A Universal Cohort Representation Learning Framework for Electronic Health Record Analysis

Electronic Health Records (EHR) are generated from clinical routine care...

Comparative Analysis of Text Classification Approaches in Electronic Health Records

Text classification tasks which aim at harvesting and/or organizing info...

Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records

The paper presents a systematic review of state-of-the-art approaches to...

Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding

The widespread availability of electronic health records (EHRs) promises...

Condensed Memory Networks for Clinical Diagnostic Inferencing

Diagnosis of a clinical condition is a challenging task, which often req...

Please sign up or login with your details

Forgot password? Click here to reset