AIMC Topic: Patient Selection

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Artificial intelligence in abdominal aortic aneurysm.

Journal of vascular surgery Feb 21, 2020
OBJECTIVE: Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be diffic...
Humans Artificial Intelligence Decision Support Techniques Clinical Decision-Making Treatment Outcome Predictive Value of Tests Aortic Aneurysm, Abdominal Patient Selection Image Interpretation, Computer-Assisted Risk Factors Risk Assessment Diagnosis, Computer-Assisted Decision Support Systems, Clinical
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Patient selection for proton therapy: a radiobiological fuzzy Markov model incorporating robust plan analysis.

Physical and engineering sciences in medicine Feb 10, 2020
While proton therapy can offer increased sparing of healthy tissue compared with X-ray therapy, it can be difficult to predict whether a benefit can be expected for an individual patient. Predictive modelling may aid in this respect. However, the pre...
Female Radiotherapy, Intensity-Modulated Quality of Life Life Expectancy Child, Preschool Fuzzy Logic Models, Theoretical Probability Proton Therapy Patient Selection Uncertainty Markov Chains Quality-Adjusted Life Years Humans
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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...
Prognosis Electronic Health Records Female Risk Factors Humans Machine Learning Databases, Factual Middle Aged Risk Assessment Disease Progression Cohort Studies Time Factors Aged Aged, 80 and over Adult Male Israel United Kingdom Canada Prediabetic State Diabetes Mellitus Follow-Up Studies Patient Selection
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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...
Time Humans Artificial Intelligence Neoplasms Patient Selection Clinical Trials as Topic
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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...
Clinical Studies as Topic Online Systems Internet-Based Intervention Robotics Substance-Related Disorders Internet Patient Selection Humans
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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...
Natural Language Processing Young Adult Epilepsy Adult Patient Selection Male Middle Aged Prospective Studies Humans Infant Child, Preschool Electronic Health Records Adolescent Child Female Machine Learning Decision Support Systems, Clinical Infant, Newborn
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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...
Male Middle Aged Risk Assessment Cohort Studies Logistic Models Follow-Up Studies Registries Percutaneous Coronary Intervention Prognosis Female Risk Factors Aged Humans Machine Learning Patient Selection Acute Coronary Syndrome
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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...
Cardiomyopathy, Hypertrophic Humans Computer Simulation Asymptomatic Diseases Machine Learning Electrocardiography Clinical Decision-Making Models, Cardiovascular Magnetic Resonance Imaging Risk Factors Death, Sudden, Cardiac Heart Ventricles Risk Assessment Patient Selection
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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...
Patient Selection Deep Learning Stents Predictive Value of Tests Humans Percutaneous Coronary Intervention Software Coronary Artery Disease Clinical Decision-Making Prosthesis Design Computer-Aided Design
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