Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia

Author:

Kaskovich Samuel1,Wyatt Kirk D.2ORCID,Oliwa Tomasz3ORCID,Graglia Luca4ORCID,Furner Brian4ORCID,Lee Jooho4ORCID,Mayampurath Anoop5ORCID,Volchenboum Samuel L.4ORCID

Affiliation:

1. Emergency Medicine Residency, Denver Health, Denver, CO

2. Department of Pediatric Hematology/Oncology, Roger Maris Cancer Center, Sanford Health, Fargo, ND

3. Center for Research Informatics, University of Chicago, Chicago, IL

4. Department of Pediatrics, University of Chicago, Chicago, IL

5. Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, WI

Abstract

PURPOSEMatching patients to clinical trials is cumbersome and costly. Attempts have been made to automate the matching process; however, most have used a trial-centric approach, which focuses on a single trial. In this study, we developed a patient-centric matching tool that matches patient-specific demographic and clinical information with free-text clinical trial inclusion and exclusion criteria extracted using natural language processing to return a list of relevant clinical trials ordered by the patient's likelihood of eligibility.MATERIALS AND METHODSRecords from pediatric leukemia clinical trials were downloaded from ClinicalTrials.gov. Regular expressions were used to discretize and extract individual trial criteria. A multilabel support vector machine (SVM) was trained to classify sentence embeddings of criteria into relevant clinical categories. Labeled criteria were parsed using regular expressions to extract numbers, comparators, and relationships. In the validation phase, a patient-trial match score was generated for each trial and returned in the form of a ranked list for each patient.RESULTSIn total, 5,251 discretized criteria were extracted from 216 protocols. The most frequent criterion was previous chemotherapy/biologics (17%). The multilabel SVM demonstrated a pooled accuracy of 75%. The text processing pipeline was able to automatically extract 68% of eligibility criteria rules, as compared with 80% in a manual version of the tool. Automated matching was accomplished in approximately 4 seconds, as compared with several hours using manual derivation.CONCLUSIONTo our knowledge, this project represents the first open-source attempt to generate a patient-centric clinical trial matching tool. The tool demonstrated acceptable performance when compared with a manual version, and it has potential to save time and money when matching patients to trials.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Implementation and evaluation of an additional GPT-4-based reviewer in PRISMA-based medical systematic literature reviews;International Journal of Medical Informatics;2024-09

2. Applying Artificial Intelligence in Pediatric Clinical Trials: Potential Impacts and Obstacles;The Journal of Pediatric Pharmacology and Therapeutics;2024-06-01

3. A Vision for Democratizing Next-Generation Oncology Clinical Trials;Cancer Discovery;2024-04-04

4. Unlocking the Power of Natural Language Processing to Automate Knowledge Discovery;2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON);2023-12-29

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