AIMC Topic: Patient Selection

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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...
Algorithms Young Adult Adult Middle Aged Infant Humans Machine Learning Epilepsy Healthcare Disparities Patient Selection Referral and Consultation Prejudice Adolescent Child, Preschool Child Age Factors Electroencephalography
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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...
Patient Compliance Humans Clinical Trials as Topic Artificial Intelligence Clinical Trials, Phase III as Topic Drug Development Clinical Protocols Patient Selection
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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.
Terminal Care Patient Selection Palliative Care Retrospective Studies Middle Aged Humans Female Risk Assessment Aged Deep Learning Aged, 80 and over Dementia Male Registries
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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.
Middle Aged Risk Assessment Disease Progression Time Factors Recovery of Function Aged Male Ventricular Function, Left Stroke Volume Echocardiography Practice Guidelines as Topic Heart-Assist Devices Heart Transplantation Ohio Baltimore Predictive Value of Tests Patient Selection Heart Failure Cardiac Resynchronization Therapy Progression-Free Survival Humans Machine Learning Decision Support Techniques Retrospective Studies Clinical Decision-Making Female Risk Factors
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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...
Cerebrovascular Circulation Prognosis Middle Cerebral Artery Clinical Decision-Making Humans Stroke Computed Tomography Angiography Predictive Value of Tests Machine Learning Patient Selection Cerebral Angiography Triage Automation Collateral Circulation Radiographic Image Interpretation, Computer-Assisted Blood Flow Velocity Thrombectomy
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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...
Keratoconus Refractive Surgical Procedures Patient Selection Humans Machine Learning Diagnosis, Computer-Assisted 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-...
Humans Clinical Decision-Making Female Signal Processing, Computer-Assisted Middle Aged Image Interpretation, Computer-Assisted Patient Selection Aged Aged, 80 and over Heart Failure Male Cardiac Resynchronization Therapy Ventricular Function, Left Echocardiography Ventricular Remodeling Electrocardiography Treatment Outcome Support Vector Machine Predictive Value of Tests
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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...
Patient Selection Gastrointestinal Hemorrhage Hemostatic Techniques Male Middle Aged Humans Risk Assessment Neural Networks, Computer Time Factors Machine Learning Clinical Decision-Making Treatment Outcome Decision Support Techniques Female Risk Factors Aged
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Using machine learning to optimize selection of elderly patients for endovascular thrombectomy.

Journal of neurointerventional surgery Feb 2, 2019
BACKGROUND: Endovascular thrombectomy (ET) is the standard of care for treatment of acute ischemic stroke (AIS) secondary to large vessel occlusion. The elderly population has been under-represented in clinical trials on ET, and recent studies have r...
Male Cohort Studies Treatment Outcome Stroke Patient Selection Endovascular Procedures Thrombectomy Machine Learning Prospective Studies Retrospective Studies Female Aged, 80 and over Humans
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Using Machine Learning to Identify Change in Surgical Decision Making in Current Use of Damage Control Laparotomy.

Journal of the American College of Surgeons Jan 9, 2019
BACKGROUND: In an earlier study, we reported the successful reduction in the use of damage control laparotomy (DCL); however, no change in the relative frequencies of specific indications was observed. In this study, we aimed to use machine learning ...
Humans Machine Learning Algorithms Retrospective Studies Clinical Decision-Making Laparotomy Middle Aged Female Abdominal Injuries Blood Transfusion Young Adult Trauma Severity Indices Blood Pressure Adult Patient Selection Sensitivity and Specificity Quality Improvement Male
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