AIMC Topic: Patient Selection

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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...
Artificial Intelligence Machine Learning Algorithms Female Adult Humans Logistic Models Mass Screening Endoscopy, Gastrointestinal Patient Selection Esophageal Diseases Duodenal Diseases Stomach Neoplasms 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...
Humans Prognosis Unsupervised Machine Learning Principal Component Analysis ST Elevation Myocardial Infarction Apolipoprotein A-I Percutaneous Coronary Intervention Cholesterol, LDL Female Aged Male Middle Aged Risk Assessment Kaplan-Meier Estimate Apolipoprotein B-100 Lipoprotein(a) Lipid Metabolism Patient Selection Patient Readmission Cholesterol, HDL Triglycerides
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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...
Schizophrenia Female Datasets as Topic Aged Antipsychotic Agents Young Adult Proof of Concept Study Humans Adult Machine Learning Male Middle Aged Adolescent Treatment Outcome Clinical Trials, Phase II as 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 Apr 18, 2021
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...
Thrombectomy Predictive Value of Tests Prognosis Thrombolytic Therapy Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Male Tomography, X-Ray Computed Stroke Humans Deep Learning Clinical Decision-Making Patient Selection Imaging, Three-Dimensional Reproducibility of Results Female Aged Aged, 80 and over
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Machine Learning and Surgical Outcomes Prediction: A Systematic Review.

The Journal of surgical research Apr 10, 2021
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...
Postoperative Complications Treatment Outcome Patient Selection Surgical Procedures, Operative Patient Care Planning Humans Machine Learning Clinical Decision-Making Risk Assessment
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Evaluating eligibility criteria of oncology trials using real-world data and AI.

Nature Apr 7, 2021
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 ...
Clinical Trials as Topic Proportional Hazards Models Clinical Laboratory Techniques Lung Neoplasms Carcinoma, Non-Small-Cell Lung Patient Selection Medical Oncology Datasets as Topic Humans Artificial Intelligence Patient Safety Electronic Health Records Reproducibility of Results
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Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network.

JAMA network open Feb 1, 2021
IMPORTANCE: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators ...
Infant, Newborn Reproducibility of Results Young Adult Adult Male Middle Aged Infant Adolescent Child, Preschool Child Machine Learning Prospective Studies Computer Simulation Retrospective Studies Female Humans ROC Curve Referral and Consultation Triage Patient Admission Area Under Curve Rare Diseases Whole Genome Sequencing Undiagnosed Diseases Patient Selection
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Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
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...
Partnership Practice Dietary Supplements Humans Natural Language Processing Databases, Factual Feasibility Studies Electronic Health Records Patient Selection Deep Learning Clinical Trials as Topic Models, Theoretical
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Selecting Children with Vesicoureteral Reflux Who are Most Likely to Benefit from Antibiotic Prophylaxis: Application of Machine Learning to RIVUR.

The Journal of urology Dec 8, 2020
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. ...
Urinary Tract Infections Patient Selection Vesico-Ureteral Reflux Antibiotic Prophylaxis Humans Male Machine Learning Infant Female Predictive Value of Tests
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Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer.

JAMA network open Nov 2, 2020
IMPORTANCE: Postoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features.
Mouth Neoplasms Patient Selection Tumor Burden Chemoradiotherapy, Adjuvant Proportional Hazards Models Humans Radiotherapy, Adjuvant Logistic Models Neural Networks, Computer Laryngeal Neoplasms Machine Learning Female Lymph Nodes Otorhinolaryngologic Surgical Procedures Retrospective Studies Aged Squamous Cell Carcinoma of Head and Neck Oropharyngeal Neoplasms Male Neoplasm Staging Hypopharyngeal Neoplasms Deep Learning Neoplasm Grading Cohort Studies
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