AIMC Topic: Machine Learning

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Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence.

Annals of internal medicine Jun 2, 2020
Increasingly, interventions aimed at improving care are likely to use such technologies as machine learning and artificial intelligence. However, health care has been relatively late to adopt them. This article provides clinical examples in which mac...
Machine Learning Humans Algorithms Artificial Intelligence Delivery of Health Care Decision Support Systems, Clinical
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Clinical applications of continual learning machine learning.

The Lancet. Digital health Jun 1, 2020
Data Management Humans Pattern Recognition, Automated Machine Learning Delivery of Health Care
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Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?

Open heart Jun 1, 2020
OBJECTIVE: Electrical cardioversion is frequently performed to restore sinus rhythm in patients with persistent atrial fibrillation (AF). However, AF recurs in many patients and identifying the patients who benefit from electrical cardioversion is di...
Patient Selection Retrospective Studies Humans Aged Clinical Decision-Making Atrial Fibrillation Recurrence Male Treatment Outcome Female Machine Learning Sex Factors Electric Countershock Recovery of Function Middle Aged Heart Disease Risk Factors Clinical Decision Rules Predictive Value of Tests Risk Assessment
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Big Data, Big Impact: The Potential for Data Science in Neurosurgery.

World neurosurgery Jun 1, 2020
Data Science Neurosurgery Artificial Intelligence Humans Machine Learning Deep Learning Big Data
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Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis.

In vivo (Athens, Greece) Jun 1, 2020
BACKGROUND/AIM: To evaluate the research trends in coronavirus disease (COVID-19).
China United States Bibliometrics Publishing Coronavirus Infections Information Dissemination Databases, Bibliographic National Institutes of Health (U.S.) Machine Learning Pandemics COVID-19 Research Humans Pneumonia, Viral Research Support as Topic
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Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care.

The Journal of pediatrics Jun 1, 2020
Intraoperative Complications Heart Arrest Airway Management Perioperative Care Hemorrhage Anesthesia Patient Admission Risk Assessment Postoperative Complications Monitoring, Intraoperative Ultrasonography Hospital Mortality Artificial Intelligence Intensive Care Units, Pediatric Humans Machine Learning Blood Transfusion Algorithms Critical Care Biomedical Research
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Editorial. Machine learning and artificial intelligence applied to the diagnosis and management of Cushing disease.

Neurosurgical focus Jun 1, 2020
Humans Neural Networks, Computer Artificial Intelligence Machine Learning Forecasting Pituitary ACTH Hypersecretion
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Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

Neurosurgical focus Jun 1, 2020
OBJECTIVE: Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing d...
Female Aged Young Adult Adult Machine Learning Forecasting Retrospective Studies Follow-Up Studies Predictive Value of Tests Nasal Cavity Neuroendoscopy Male Middle Aged Adolescent Treatment Outcome Humans Pituitary ACTH Hypersecretion
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Incremental inputs improve the automated detection of implant loosening using machine-learning algorithms.

The bone & joint journal Jun 1, 2020
AIMS: The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance.
Radiography Knee Prosthesis Prosthesis Failure Humans Machine Learning Aged Algorithms Male Female Middle Aged Postoperative Complications
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Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.

Journal of animal science Jun 1, 2020
The aim of this study was to compare the predictive performance of the Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods (Random Forest, RF; Support Vector Machine, SVM; Artificial Neural Network, ANN) in simulated populatio...
Machine Learning Phenotype Computer Simulation Female Breeding Animals Genome Male Multifactorial Inheritance Genomics Genes, Dominant Genotype
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