Harnessing explainable artificial intelligence for patient-to-clinical-trial matching: A proof-of-concept pilot study using phase I oncology trials.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 24 01 2024
accepted: 19 09 2024
medline: 25 10 2024
pubmed: 25 10 2024
entrez: 24 10 2024
Statut: epublish

Résumé

This study aims to develop explainable AI methods for matching patients with phase 1 oncology clinical trials using Natural Language Processing (NLP) techniques to address challenges in patient recruitment for improved efficiency in drug development. A prototype system based on modern NLP techniques has been developed to match patient records with phase 1 oncology clinical trial protocols. Four criteria are considered for the matching: cancer type, performance status, genetic mutation, and measurable disease. The system outputs a summary matching score along with explanations of the evidence. The outputs of the AI system were evaluated against the ground truth matching results provided by the domain expert on a dataset of twelve synthesized dummy patient records and six clinical trial protocols. The system achieved a precision of 73.68%, sensitivity/recall of 56%, accuracy of 77.78%, and specificity of 89.36%. Further investigation into the misclassified cases indicated that ambiguity of abbreviation and misunderstanding of context are significant contributors to errors. The system found evidence of no matching for all false positive cases. To the best of our knowledge, no system in the public domain currently deploys an explainable AI-based approach to identify optimal patients for phase 1 oncology trials. This initial attempt to develop an AI system for patients and clinical trial matching in the context of phase 1 oncology trials showed promising results that are set to increase efficiency without sacrificing quality in patient-trial matching.

Identifiants

pubmed: 39446771
doi: 10.1371/journal.pone.0311510
pii: PONE-D-24-02458
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0311510

Informations de copyright

Copyright: © 2024 Ghosh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Satanu Ghosh (S)

Department of Computer Science, University of New Hampshire, Durham, New Hampshire, United States of America.

Hassan Mohammed Abushukair (HM)

Jordan University of Science and Technology, Irbid, Jordan.

Arjun Ganesan (A)

School of Computer Science, University of Oklahoma Norman Campus, Norman, Oklahoma, United States of America.

Chongle Pan (C)

School of Computer Science, University of Oklahoma Norman Campus, Norman, Oklahoma, United States of America.

Abdul Rafeh Naqash (AR)

Medical Oncology/ TSET Phase 1 Program, Stephenson Cancer Center, The University of Oklahoma Health Science Campus, Oklahoma City, Oklahoma, United States of America.

Kun Lu (K)

School of Library and Information Studies, University of Oklahoma Norman Campus, Norman, Oklahoma, United States of America.

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