Open AccessImproving the Efficiency of Clinical Trial Recruitment Using an Ensemble Machine Learning to Assist With Eligibility Screening
Author(s) -
Cai Tianrun,
Cai Fiona,
Dahal Kumar P.,
Cremone Gabrielle,
Lam Ethan,
Golnik Charlotte,
Seyok Thany,
Hong Chuan,
Cai Tianxi,
Liao Katherine P.
Publication year - 2021
Publication title -
acr open rheumatology
Language(s) - English
Resource type - Journals
ISSN - 2578-5745
DOI - 10.1002/acr2.11289
Subject(s) - medicine , artificial intelligence , electronic health record , machine learning , random forest , logistic regression , health records , computer science , software portability , clinical trial , health care , natural language processing , economics , programming language , economic growth
Objective Efficiently identifying eligible patients is a crucial first step for a successful clinical trial. The objective of this study was to test whether an approach using electronic health record (EHR) data and an ensemble machine learning algorithm incorporating billing codes and data from clinical notes processed by natural language processing (NLP) can improve the efficiency of eligibility screening. Methods We studied patients screened for a clinical trial of rheumatoid arthritis (RA) with one or more International Classification of Diseases (ICD) code for RA and age greater than 35 years, from a tertiary care center and a community hospital. The following three groups of EHR features were considered for the algorithm: 1 ) structured features, 2 ) the counts of NLP concepts from notes, 3 ) health care utilization. All features were linked to dates. We applied random forest and logistic regression with least absolute shrinkage and selection operator penalty against the following two standard approaches: 1 ) one or more RA ICD code and no ICD codes related to exclusion criteria (Screen RAICD1 +EX ) and 2 ) two or more RA ICD codes (Screen RAICD2 ). To test the portability, we trained the algorithm at one institution and tested it at the other. Results In total, 3359 patients at Brigham and Women’s Hospital (BWH) and 642 patients at Faulkner Hospital (FH) were studied, with 461 (13.7%) eligible patients at BWH and 84 (13.4%) at FH. The application of the algorithm reduced ineligible patients from chart review by 40.5% at the tertiary care center and by 57.0% at the community hospital. In contrast, Screen RAICD2 reduced patients for chart review by 2.7% to 11.3%; Screen RAICD1+EX reduced patients for chart review by 63% to 65% but excluded 22% to 27% of eligible patients. Conclusion The ensemble machine learning algorithm incorporating billing codes and NLP data increased the efficiency of eligibility screening by reducing the number of patients requiring chart review while not excluding eligible patients. Moreover, this approach can be trained at one institution and applied at another for multicenter clinical trials.