AIMC Topic: Patient Selection

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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...
Humans Research Design Clinical Trials as Topic Eligibility Determination Cluster Analysis Large Language Models Natural Language Processing Clinical Protocols Patient Selection
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Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2024
OBJECTIVE: The objective of our research is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of artificial intelligence (AI) in the recruitment and retention of participants in clinica...
Patient Selection Clinical Trials as Topic Humans Artificial Intelligence
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Using Natural Language Processing on Expert Panel Discussions to Gain Insights for Recruitment, Retention and Intervention Adherence for Online Social Support Interventions on a Stage II-III Clinical Trial Among Hispanic and African American Dementia Caregivers.

Studies in health technology and informatics Aug 22, 2024
We applied natural language processing (NLP) to a corpus extracted from 4 hours of expert panel discussion transcripts to determine the sustainability of a Stage II-III clinical trial of online social support interventions for Hispanic and African Am...
Caregivers Social Support Dementia Natural Language Processing Hispanic or Latino Humans Internet Black or African American Patient Selection Aged
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RE: Use of artificial intelligence for cancer clinical trial enrollment.

Journal of the National Cancer Institute Jan 10, 2024
Patient Selection Humans Artificial Intelligence Neoplasms
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Machine Learning in Pain Neuromodulation.

Advances in experimental medicine and biology Jan 1, 2024
This chapter highlights the intersection of pain neuromodulation and machine learning (ML), exploring current limitations in pain management and how ML techniques can address these challenges. Neuromodulation technologies, such as spinal cord stimula...
Patient Selection Pain Management Chronic Pain Spinal Cord Stimulation Humans Machine Learning Algorithms
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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...
Patient Selection Clinical Trials as Topic Eligibility Determination Leukemia Child Natural Language Processing Patient-Centered Care Humans
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Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Briefings in bioinformatics Sep 2, 2021
Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection...
Prognosis Female Aged Brain Neoplasms Male Adult Tumor Microenvironment Glioblastoma Humans Immunotherapy Machine Learning Patient Selection Gene Expression Regulation, Neoplastic Antineoplastic Agents, Alkylating Programmed Cell Death 1 Receptor Atlases as Topic Treatment Outcome Survival Analysis Middle Aged Neoplastic Stem Cells Temozolomide Gene Expression Profiling Transcriptome
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Machine learning application for patient stratification and phenotype/genotype investigation in a rare disease.

Briefings in bioinformatics Sep 2, 2021
Alkaptonuria (AKU, OMIM: 203500) is an autosomal recessive disorder caused by mutations in the Homogentisate 1,2-dioxygenase (HGD) gene. A lack of standardized data, information and methodologies to assess disease severity and progression represents ...
Databases, Genetic Mutation Patient Selection Homogentisate 1,2-Dioxygenase Rare Diseases Alkaptonuria Female Male Genotype Humans Machine Learning Precision Medicine
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Editorial: Designing a Protocol Adopting an Artificial Intelligence (AI)-Driven Approach for Early Diagnosis of Late-Onset Alzheimer's Disease.

Journal of molecular neuroscience : MN Jul 1, 2021
Mutation Models, Biological Patient Selection Humans Case-Control Studies Mobile Applications Biomarkers Wearable Electronic Devices Artificial Intelligence Cognition Language Observational Studies as Topic Asian People Prospective Studies Medical History Taking Alzheimer Disease Early Diagnosis Memory Ethnicity Hong Kong Clinical Trial Protocols as Topic Behavior Mental Status and Dementia Tests Late Onset Disorders
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Natural Language Processing for Patient Selection in Phase I or II Oncology Clinical Trials.

JCO clinical cancer informatics Jun 1, 2021
PURPOSE: Early discontinuation affects more than one third of patients enrolled in early-phase oncology clinical trials. Early discontinuation is deleterious both for the patient and for the study, by inflating its duration and associated costs. We a...
Natural Language Processing Humans Patient Selection Machine Learning Medical Oncology Neoplasms
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