AIMC Topic: Patient Selection
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Ranking patients on the kidney transplant waiting list based on fuzzy inference system.
BMC nephrology
Jan 15, 2022
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 ...
Optimizing hepatitis B virus screening in the United States using a simple demographics-based model.
Hepatology (Baltimore, Md.)
Dec 7, 2021
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...
The path to precision medicine for MS, from AI to patient recruitment: an interview with Mauricio Farez and Helen Onuorah.
Communications biology
Jul 22, 2021
This year’s World Brain Day is focused on stopping Multiple Sclerosis (MS). Although amazing progress has resulted in the development of relatively successful MS therapies, access to such therapies is a major problem for most of the world. In additio...
Emerging role of artificial intelligence in stroke imaging.
Expert review of neurotherapeutics
Jul 20, 2021
: The recognition and therapy of patients with stroke is becoming progressively intricate as additional treatment choices become accessible and new associations between disease characteristics and treatment response are incessantly uncovered. Therefo...
Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department.
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Jul 9, 2021
BACKGROUND: Patients with Crohn's disease (CD) frequently undergo abdominopelvic computed tomography (APCT) in the emergency department (ED). It's essential to diagnose clinically actionable findings (CAF) as they may need immediate intervention, fre...
An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation.
Frontiers in immunology
Jul 8, 2021
Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdr...
Transplantation Tolerance
Graft Rejection
Patient Selection
Oligonucleotide Array Sequence Analysis
Transcriptome
Time Factors
Graft Survival
Treatment Outcome
Databases, Genetic
Immunosuppressive Agents
Predictive Value of Tests
Clinical Decision-Making
Risk Factors
Risk Assessment
Gene Expression Profiling
Humans
Machine Learning
Kidney Transplantation
Learning and confirming a class of treatment responders in clinical trials.
Statistics in medicine
Jun 14, 2021
Clinical trials require substantial effort and time to complete, and regulatory agencies may require two successful efficacy trials before approving a new drug. One way to improve the chance of follow-up success is to identify a subpopulation among w...
Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial.
American heart journal
May 24, 2021
BACKGROUND: Clinical trials are a fundamental tool to evaluate medical interventions but are time-consuming and resource-intensive.
Can methods of artificial intelligence aid in optimizing patient selection in patients undergoing intrauterine inseminations?
Journal of assisted reproduction and genetics
May 24, 2021
PURPOSE: AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study was to construct several predictive models based on clinical data and select the bes...
Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy.
BMC urology
May 16, 2021
BACKGROUND: Machine learning has many attractive theoretic properties, specifically, the ability to handle non predefined relations. Additionally, studies have validated the clinical utility of mpMRI for the detection and localization of CSPCa (Gleas...
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