AIMC Topic: Eligibility Determination
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Physician Level Assessment of Hirsute Women and of Their Eligibility for Laser Treatment With Deep Learning.
Lasers in surgery and medicine
Sep 22, 2024
OBJECTIVES: Hirsutism is a widespread condition affecting 5%-15% of females. Laser treatment of hirsutism has the best long-term effect. Patients with nonpigmented or nonterminal hairs are not eligible for laser treatment, and the current patient jou...
[Development of an artificial intelligence system to improve cancer clinical trial eligibility screening].
Bulletin du cancer
Mar 19, 2024
INTRODUCTION: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could improve recruitment in order to shorten inclusion phase. The objective was to assess the performance of a...
Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review.
International journal of medical informatics
May 4, 2023
OBJECTIVE: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objecti...
Parsable Clinical Trial Eligibility Criteria Representation Using Natural Language Processing.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 29, 2023
Successful clinical trials offer better treatments to current or future patients and advance scientific research. Clinical trials define the target population using specific eligibility criteria to ensure an optimal enrollment sample. Clinical trial ...
Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design.
Scientific reports
Jan 12, 2023
Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data ...
A review of research on eligibility criteria for clinical trials.
Clinical and experimental medicine
Jan 5, 2023
The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the...
Improving clinical trial design using interpretable machine learning based prediction of early trial termination.
Scientific reports
Jan 4, 2023
This study proposes using a machine learning pipeline to optimise clinical trial design. The goal is to predict early termination probability of clinical trials using machine learning modelling, and to understand feature contributions driving early t...
Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials.
Journal of cardiovascular translational research
Jun 5, 2017
Precision medicine requires clinical trials that are able to efficiently enroll subtypes of patients in whom targeted therapies can be tested. To reduce the large amount of time spent screening, identifying, and recruiting patients with specific subt...
Automatic data source identification for clinical trial eligibility criteria resolution.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 10, 2017
Clinical trial coordinators refer to both structured and unstructured sources of data when evaluating a subject for eligibility. While some eligibility criteria can be resolved using structured data, some require manual review of clinical notes. An i...
Textual inference for eligibility criteria resolution in clinical trials.
Journal of biomedical informatics
Sep 14, 2015
Clinical trials are essential for determining whether new interventions are effective. In order to determine the eligibility of patients to enroll into these trials, clinical trial coordinators often perform a manual review of clinical notes in the e...
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