AIMC Topic: Eligibility Determination
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Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients.
BMC medical informatics and decision making
Apr 14, 2015
BACKGROUND: Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Leveraging state-of-the-art natural language processing (NLP) and information extraction (...
Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.
Journal of the American Medical Informatics Association : JAMIA
Jul 16, 2014
OBJECTIVES: (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extractio...
Easy ensemble classifier-group and intersectional fairness and threshold (EEC-GIFT): a fairness-aware machine learning framework for lung cancer screening eligibility using real-world data.
JNCI cancer spectrum
Mar 3, 2025
BACKGROUND: We use real-world data to develop a lung cancer screening (LCS) eligibility mechanism that is both accurate and free from racial bias.
Analysis of eligibility criteria clusters based on large language models for clinical trial design.
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2025
OBJECTIVES: Clinical trials (CTs) are essential for improving patient care by evaluating new treatments' safety and efficacy. A key component in CT protocols is the study population defined by the eligibility criteria. This study aims to evaluate the...
Implementation of Artificial Intelligence Applications in Australian Healthcare Organisations: Environmental Scan Findings.
Studies in health technology and informatics
Jan 25, 2024
Artificial Intelligence (AI) has great potential to improve healthcare, but implementation into routine practice remains a challenge. This study scoped the extent to which AI and Natural Language Processing (NLP) is being implemented into routine pra...
AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models.
Journal of the American Medical Informatics Association : JAMIA
Jan 18, 2024
OBJECTIVES: We aim to build a generalizable information extraction system leveraging large language models to extract granular eligibility criteria information for diverse diseases from free text clinical trial protocol documents. We investigate the ...
Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia.
JCO clinical cancer informatics
Jul 1, 2023
PURPOSE: Matching 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...
Comment on: "The impact of age on pathological insignificant prostate cancer rates in contemporary robot-assisted prostatectomy patients despite active surveillance eligibility".
Minerva urology and nephrology
Aug 1, 2022
Evaluation of Criteria2Query: Towards Augmented Intelligence for Cohort Identification.
Studies in health technology and informatics
Jun 6, 2022
Electronic healthcare records data promises to improve the efficiency of patient eligibility screening, which is an important factor in the success of clinical trials and observational studies. To bridge the sociotechnical gap in cohort identificatio...
A systematic review on natural language processing systems for eligibility prescreening in clinical research.
Journal of the American Medical Informatics Association : JAMIA
Dec 28, 2021
OBJECTIVE: We conducted a systematic review to assess the effect of natural language processing (NLP) systems in improving the accuracy and efficiency of eligibility prescreening during the clinical research recruitment process.
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