The Dependence of Machine Learning on Electronic Medical Record Quality

2017/03/23 by Van Long Ho, David Ledbetter, Ho, Long +5 · 1 citation
Computer Science · Medicine · #FOS: Computer and information sciences #Heart Failure Treatment and Management #Machine Learning (stat.ML) #Machine Learning in Healthcare #Sepsis Diagnosis and Treatment

paper · doi:10.48550/arxiv.1703.08251

openalex publication_date 2017/03/23 · openalex created_date 2018/02/02 · openalex updated_date 2026/07/29

Abstract

There is growing interest in applying machine learning methods to Electronic Medical Records (EMR). Across different institutions, however, EMR quality can vary widely. This work investigated the impact of this disparity on the performance of three advanced machine learning algorithms: logistic regression, multilayer perceptron, and recurrent neural network. The EMR disparity was emulated using different permutations of the EMR collected at Children's Hospital Los Angeles (CHLA) Pediatric Intensive Care Unit (PICU) and Cardiothoracic Intensive Care Unit (CTICU). The algorithms were trained using patients from the PICU to predict in-ICU mortality for patients on a held out set of PICU and CTICU patients. The disparate patient populations between the PICU and CTICU provide an estimate of generalization errors across different ICUs. We quantified and evaluated the generalization of these algorithms on varying EMR size, input types, and fidelity of data.

Citations

• Long Short-Term Memory

Cited by

• Predicting High-Flow Nasal Cannula Failure in an ICU Using a Recurrent Neural Network with Transfer Learning and Input Data Perseveration: A Retrospective Analysis

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