AIMC Topic: Patient Selection

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Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

Diabetes/metabolism research and reviews Jan 14, 2020
AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whethe...
Male Middle Aged Risk Assessment Disease Progression Risk Factors Aged Aged, 80 and over Adult Follow-Up Studies Patient Selection Israel United Kingdom Prediabetic State Cohort Studies Time Factors Canada Diabetes Mellitus Humans Machine Learning Databases, Factual Prognosis Electronic Health Records Female
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Improving Clinical Trial Participant Prescreening With Artificial Intelligence (AI): A Comparison of the Results of AI-Assisted vs Standard Methods in 3 Oncology Trials.

Therapeutic innovation & regulatory science Jan 6, 2020
BACKGROUND: Delays in clinical trial enrollment and difficulties enrolling representative samples continue to vex sponsors, sites, and patient populations. Here we investigated use of an artificial intelligence-powered technology, Mendel.ai, as a mea...
Clinical Trials as Topic Time Humans Artificial Intelligence Neoplasms Patient Selection
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Out damn bot, out: Recruiting real people into substance use studies on the internet.

Substance abuse Dec 10, 2019
While the Internet has become a popular and effective strategy for recruiting substance users into research, there is a large risk of recruiting duplicate individuals and Internet bots that pose as humans. Strategies to mitigate these issues are outl...
Humans Robotics Substance-Related Disorders Internet Patient Selection Clinical Studies as Topic Online Systems Internet-Based Intervention
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Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.

Epilepsia Nov 29, 2019
OBJECTIVE: Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epileps...
Middle Aged Infant Adolescent Child, Preschool Infant, Newborn Young Adult Adult Male Patient Selection Child Decision Support Systems, Clinical Natural Language Processing Epilepsy Humans Machine Learning Prospective Studies Electronic Health Records Female
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Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Scientific reports Oct 10, 2019
Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We firs...
Percutaneous Coronary Intervention Patient Selection Acute Coronary Syndrome Cohort Studies Prognosis Logistic Models Female Follow-Up Studies Aged Risk Factors Registries Male Humans Middle Aged Machine Learning Risk Assessment
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Can machine learning improve patient selection for cardiac resynchronization therapy?

PloS one Oct 3, 2019
RATIONALE: Multiple clinical trials support the effectiveness of cardiac resynchronization therapy (CRT); however, optimal patient selection remains challenging due to substantial treatment heterogeneity among patients who meet the clinical practice ...
Cardiac Resynchronization Therapy Outcome Assessment, Health Care Male Humans ROC Curve Machine Learning Models, Theoretical Female Patient Selection Aged
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Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study.

Morphologie : bulletin de l'Association des anatomistes Sep 27, 2019
Most patients with hypertrophic cardiomyopathy (HCM), the most common genetic cardiac disease, remain asymptomatic, but others may suffer from sudden cardiac death. A better identification of those patients at risk, together with a better understandi...
Asymptomatic Diseases Models, Cardiovascular Death, Sudden, Cardiac Magnetic Resonance Imaging Heart Ventricles Patient Selection Cardiomyopathy, Hypertrophic Clinical Decision-Making Risk Factors Humans Risk Assessment Machine Learning Electrocardiography Computer Simulation
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Deep Learning in Personalization of Cardiovascular Stents.

Journal of cardiovascular pharmacology and therapeutics Sep 25, 2019
Deep learning (DL) application has demonstrated its enormous potential in accomplishing biomedical tasks, such as vessel segmentation, brain visualization, and speech recognition. This review article has mainly covered recent advances in the principl...
Software Clinical Decision-Making Computer-Aided Design Deep Learning Predictive Value of Tests Percutaneous Coronary Intervention Coronary Artery Disease Prosthesis Design Humans Patient Selection Stents
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Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method.

BMC medical informatics and decision making Sep 6, 2019
BACKGROUND: Kidney allocation is a multi-criteria and complex decision-making problem, which should also consider ethical issues in addition to the medical aspects. Leading countries in this field use a point scoring system to allocate kidneys. Hence...
Iran Tissue and Organ Procurement Uncertainty Resource Allocation Kidney Transplantation Fuzzy Logic Humans Decision Making Algorithms Patient Selection
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Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation.

Annals of neurology Aug 27, 2019
OBJECTIVE: Vagus nerve stimulation (VNS) is a common treatment for medically intractable epilepsy, but response rates are highly variable, with no preoperative means of identifying good candidates. This study aimed to predict VNS response using struc...
Connectome Diffusion Tensor Imaging Drug Resistant Epilepsy Magnetoencephalography Treatment Outcome Male Support Vector Machine Humans Patient Selection Vagus Nerve Stimulation Adolescent Image Interpretation, Computer-Assisted Female Child, Preschool Child
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