AIMC Topic: Patient Selection

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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...
Prognosis Humans Clinical Decision-Making Stroke Artificial Intelligence Patient Selection
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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...
Humans Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed Young Adult Adolescent ROC Curve Emergency Service, Hospital Crohn Disease Patient Selection Adult Male Middle Aged Risk Assessment
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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 Time Factors Graft Survival Treatment Outcome Graft Rejection Risk Factors Humans Databases, Genetic Immunosuppressive Agents Patient Selection Machine Learning Predictive Value of Tests Risk Assessment Oligonucleotide Array Sequence Analysis Kidney Transplantation Gene Expression Profiling Clinical Decision-Making Transcriptome
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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...
Humans Machine Learning Clinical Trials as Topic Learning Patient Selection
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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.
Female Humans Undiagnosed Diseases Nervous System Diseases Remote Sensing Technology Adult Artificial Intelligence Outcome and Process Assessment, Health Care Patient Selection Algorithms Male Atrial Fibrillation Diagnosis, Computer-Assisted Monitoring, Ambulatory
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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...
Insemination, Artificial Letrozole Decision Making, Computer-Assisted Clomiphene Fertility Agents, Female Humans Gonadotropins Neural Networks, Computer Male Support Vector Machine Spermatozoa Patient Selection Artificial Intelligence Pregnancy Female Adult
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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...
Humans Machine Learning Retrospective Studies Prostate Aged Biopsy Patient Selection Male Multiparametric Magnetic Resonance Imaging Middle Aged Models, Theoretical
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Using machine-learning algorithms to identify patients at high risk of upper gastrointestinal lesions for endoscopy.

Journal of gastroenterology and hepatology May 10, 2021
BACKGROUND AND AIM: Endoscopic screening for early detection of upper gastrointestinal (UGI) lesions is important. However, population-based endoscopic screening is difficult to implement in populous countries. By identifying high-risk individuals fr...
Machine Learning Algorithms Female Adult Stomach Neoplasms Humans Artificial Intelligence Esophageal Diseases Duodenal Diseases Logistic Models Mass Screening Endoscopy, Gastrointestinal Patient Selection Male Middle Aged Risk Assessment China
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Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

Lipids in health and disease May 6, 2021
BACKGROUND: Numerous studies have revealed the relationship between lipid expression and increased cardiovascular risk in ST-segment elevation myocardial infarction (STEMI) patients. Nevertheless, few investigations have focused on the risk stratific...
Kaplan-Meier Estimate Apolipoprotein B-100 Lipoprotein(a) Patient Selection Patient Readmission Cholesterol, HDL Triglycerides Prognosis Apolipoprotein A-I Female Percutaneous Coronary Intervention Aged Unsupervised Machine Learning Male Middle Aged Cholesterol, LDL Principal Component Analysis Humans Lipid Metabolism ST Elevation Myocardial Infarction Risk Assessment
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Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical and translational science May 3, 2021
Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a m...
Clinical Trials, Phase II as Topic Patient Selection Schizophrenia Datasets as Topic Male Middle Aged Adolescent Treatment Outcome Antipsychotic Agents Proof of Concept Study Humans Aged Young Adult Adult Machine Learning Female
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