ArticleJournal of biomedical informatics2023
A data-driven approach to optimizing clinical study eligibility criteria.
Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025Review
- CLEAR: A vision to support clinical evidence lifecycle with continuous learning.Journal of biomedical informatics · 2025Article
- Optimizing Exclusion Criteria for Clinical Trials of Persistent Lyme Disease Using Real-World Data.Healthcare (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveFeasible, safe, and inclusive eligibility criteria are crucial to successful clinical research recruitment. Existing expert-centered methods for eligibility criteria selection may not be representative of real-world populations. This paper presents a novel model called OPTEC (OPTimal Eligibility Criteria) based on the Multiple Attribute Decision Making method boosted by an efficient greedy algorithm.
methodsIt systematically identifies the optimal criteria combination for a given medical condition with the optimal tradeoff among feasibility, patient safety, and cohort diversity. The model offers flexibility in attribute configurations and generalizability to various clinical domains. The model was evaluated on two clinical domains (i.e., Alzheimer's disease and Neoplasm of pancreas) using two datasets (i.e., MIMIC-III dataset and NewYork-Presbyterian/Columbia University Irving Medical Center (NYP/CUIMC) database).
resultsWe simulated the process of automatically optimizing eligibility criteria according to user-specified prioritization preferences and generated recommendations based on the top-ranked criteria combination accordingly (top 0.41-2.75%) with OPTEC. Harnessing the power of the model, we designed an interactive criteria recommendation system and conducted a case study with an experienced clinical researcher using the think-aloud protocol.
conclusionsThe results demonstrated that OPTEC could be used to recommend feasible eligibility criteria combinations, and to provide actionable recommendations for clinical study designers to construct a feasible, safe, and diverse cohort definition during early study design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.