AIMC Topic: Patient Selection

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Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a promising tool for the evaluation of stroke expansion to determine suitability for reperfusion therapy. The aim of this study was to validate deep learning-based AS...

Machine Learning and Surgical Outcomes Prediction: A Systematic Review.

The Journal of surgical research
BACKGROUND: Machine learning (ML) has garnered increasing attention as a means to quantitatively analyze the growing and complex medical data to improve individualized patient care. We herein aim to critically examine the current state of ML in predi...

Evaluating eligibility criteria of oncology trials using real-world data and AI.

Nature
There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes ...

Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, ca...

Selecting Children with Vesicoureteral Reflux Who are Most Likely to Benefit from Antibiotic Prophylaxis: Application of Machine Learning to RIVUR.

The Journal of urology
PURPOSE: Continuous antibiotic prophylaxis reduces the risk of recurrent urinary tract infection by 50% in children with vesicoureteral reflux. However, there may be subgroups in whom continuous antibiotic prophylaxis could be used more selectively. ...

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

Scientific reports
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a...

Cohort selection for clinical trials using multiple instance learning.

Journal of biomedical informatics
Identifying patients eligible for clinical trials using electronic health records (EHRs) is a challenging task usually requiring a comprehensive analysis of information stored in multiple EHRs of a patient. The goal of this study is to investigate di...