AIMC Topic: Patient Selection

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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...
Patient Selection Iran Tissue and Organ Procurement Uncertainty Resource Allocation Humans Algorithms Kidney Transplantation Fuzzy Logic Decision Making
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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...
Humans Connectome Female Diffusion Tensor Imaging Male Adolescent Child, Preschool Child Treatment Outcome Support Vector Machine Patient Selection Image Interpretation, Computer-Assisted Drug Resistant Epilepsy Magnetoencephalography Vagus Nerve Stimulation
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Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.

Epilepsia Aug 23, 2019
Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluation...
Humans Machine Learning Algorithms Young Adult Healthcare Disparities Patient Selection Referral and Consultation Prejudice Child, Preschool Child Adult Age Factors Middle Aged Electroencephalography Infant Epilepsy Adolescent
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Artificial Intelligence for Clinical Trial Design.

Trends in pharmacological sciences Jul 17, 2019
Clinical trials consume the latter half of the 10 to 15 year, 1.5-2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical developm...
Clinical Protocols Humans Artificial Intelligence Drug Development Patient Selection Patient Compliance Clinical Trials as Topic Clinical Trials, Phase III as Topic
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Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions.

JAMA network open Jul 3, 2019
IMPORTANCE: Early palliative care interventions drive high-value care but currently are underused. Health care professionals face challenges in identifying patients who may benefit from palliative care.
Aged Aged, 80 and over Male Middle Aged Risk Assessment Humans Retrospective Studies Female Palliative Care Deep Learning Dementia Registries Terminal Care Patient Selection
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Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.

Circulation. Arrhythmia and electrophysiology Jun 20, 2019
BACKGROUND: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines.
Ohio Baltimore Cardiac Resynchronization Therapy Risk Factors Progression-Free Survival Aged Practice Guidelines as Topic Male Echocardiography Machine Learning Heart-Assist Devices Recovery of Function Ventricular Function, Left Middle Aged Humans Decision Support Techniques Stroke Volume Heart Transplantation Predictive Value of Tests Risk Assessment Patient Selection Disease Progression Retrospective Studies Heart Failure Time Factors Clinical Decision-Making Female
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Collateral Automation for Triage in Stroke: Evaluating Automated Scoring of Collaterals in Acute Stroke on Computed Tomography Scans.

Cerebrovascular diseases (Basel, Switzerland) Jun 19, 2019
Computed tomography angiography (CTA) collateral scoring can identify patients most likely to benefit from mechanical thrombectomy and those more likely to have good outcomes and ranges from 0 (no collaterals) to 3 (complete collaterals). In this stu...
Cerebral Angiography Collateral Circulation Radiographic Image Interpretation, Computer-Assisted Prognosis Thrombectomy Stroke Humans Clinical Decision-Making Patient Selection Cerebrovascular Circulation Predictive Value of Tests Machine Learning Automation Middle Cerebral Artery Triage Blood Flow Velocity Computed Tomography Angiography
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A Review of Machine Learning Techniques for Keratoconus Detection and Refractive Surgery Screening.

Seminars in ophthalmology Jun 9, 2019
Various machine learning techniques have been developed for keratoconus detection and refractive surgery screening. These techniques utilize inputs from a range of corneal imaging devices and are built with automated decision trees, support vector ma...
Humans Keratoconus Machine Learning Refractive Surgical Procedures Diagnosis, Computer-Assisted Patient Selection Mass Screening
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Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.

The international journal of cardiovascular imaging May 18, 2019
Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to find out novel analytical approaches to improve pre-implantation CRT response prediction. Among 31 pre-...
Ventricular Remodeling Image Interpretation, Computer-Assisted Signal Processing, Computer-Assisted Humans Patient Selection Clinical Decision-Making Middle Aged Heart Failure Aged Female Electrocardiography Cardiac Resynchronization Therapy Aged, 80 and over Treatment Outcome Male Support Vector Machine Ventricular Function, Left Predictive Value of Tests Echocardiography
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Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review.

Digestive diseases and sciences May 4, 2019
Risk stratification of patients with gastrointestinal bleeding (GIB) is recommended, but current risk assessment tools have variable performance. Machine learning (ML) has promise to improve risk assessment. We performed a systematic review to evalua...
Decision Support Techniques Clinical Decision-Making Female Risk Factors Aged Male Middle Aged Risk Assessment Time Factors Treatment Outcome Humans Patient Selection Neural Networks, Computer Gastrointestinal Hemorrhage Machine Learning Hemostatic Techniques
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