Work

A Machine Learning Approach to Predicting the Stability of Inpatient Lab Test Results.

Year: 2019

Type: article

Abstract: A primary focus for reducing waste in healthcare expenditure is identifying and discouraging unnecessary repeat lab tests. A machine learning model which could reliably predict low information lab tests could provide personalized, real-time predictions to discourage over-testing. To this end, we apply six standard machine learning algorithms to six years (2008-2014) of inpatient data from a tertiary academic center, to predict when the next measurement of a lab test is likely to be the "same" as the previous one. Out of 13 common inpatient lab tests selected for this analysis, several are predictably stable in many cases. This points to potential areas where machine learning approaches may identify and prevent unneeded testing before it occurs, and a methodological framework for how these tasks can be accomplished.

Source: PubMed Central

Authors: Rachael C Aikens, Santhosh Balasubramanian, Jonathan H Chen

Institutions: Stanford Medicine, Stanford University

Language: English

Topic: Healthcare cost, quality, practices

Subfield: General Health Professions

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institute of Environmental Health Sciences, U.S. National Library of Medicine

Awards: Data-Mining Clinical Decision Support from Electronic Health Records, Biomedical Data Science Graduate Training at Stanford