AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Patient Selection

Showing 51 to 60 of 156 articles

Clear Filters

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
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...

Machine Learning in Pain Neuromodulation.

Advances in experimental medicine and biology
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...

Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia.

JCO clinical cancer informatics
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...

The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria.

Scientific data
Identifying cohorts of patients based on eligibility criteria such as medical conditions, procedures, and medication use is critical to recruitment for clinical trials. Such criteria are often most naturally described in free-text, using language fam...

Machine Learning Prediction of Clinical Trial Operational Efficiency.

The AAPS journal
Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, ...

Optimizing hepatitis B virus screening in the United States using a simple demographics-based model.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: Chronic hepatitis B (CHB) affects >290 million persons globally, and only 10% have been diagnosed, presenting a severe gap that must be addressed. We developed logistic regression (LR) and machine learning (ML; random forest) mod...

Hospital Ownership, Geographic Region, Patient Age, Comorbidities, and Insurance Status Appear to Influence Patient Selection Robot-Assisted Ureteral Reimplantation for Benign Disease: A Population-Based Analysis.

Journal of endourology
Robot-assisted ureteral reimplantation (RAUR) is a relatively new minimally invasive procedure. As such, research is lacking, and the largest adult cohort studies include fewer than 30 patients. Our aim was to be the first population-based study to ...

Ranking patients on the kidney transplant waiting list based on fuzzy inference system.

BMC nephrology
BACKGROUND: Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed ...

Prediction of clinical trial enrollment rates.

PloS one
Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long standing problem affecting institutions all over the world. One way to reduce the cost incurred by ins...