AIMC Topic: Algorithms

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Digitalisation of the Brief Visuospatial Memory Test-Revised and Evaluation with a Machine Learning Algorithm.

Studies in health technology and informatics Jun 16, 2020
The disease multiple sclerosis (MS) is characterized by various neurological symptoms. This paper deals with a novel tool to assess cognitive dysfunction. The Brief Visuospatial Memory Test-Revised (BVMT-R) is a recognized method to measure optical r...
Memory Humans Reproducibility of Results Machine Learning Neuropsychological Tests Algorithms
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Clinical Abbreviation Disambiguation Using Deep Contextualized Representation.

Studies in health technology and informatics Jun 16, 2020
The objective of this study is to develop a method for clinical abbreviation disambiguation using deep contextualized representation and cluster analysis. We employed the pre-trained BioELMo language model to generate the contextualized word vector f...
Algorithms Language Cluster Analysis Natural Language Processing
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Blood Lactate Concentration Prediction in Critical Care.

Studies in health technology and informatics Jun 16, 2020
Blood lactate concentration is a reliable risk indicator of deterioration in critical care requiring frequent blood sampling. However, lactate measurement is an invasive procedure that can increase risk of infections. Yet there is no clinical consens...
Critical Care Lactic Acid Machine Learning Humans Algorithms Risk Factors
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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...
Decision Support Systems, Clinical Machine Learning Algorithms Delivery of Health Care Humans Artificial Intelligence
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Action-Informed Artificial Intelligence-Matching the Algorithm to the Problem.

JAMA Jun 2, 2020
Algorithms Delivery of Health Care Decision Making, Computer-Assisted Humans Artificial Intelligence
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A talker-independent deep learning algorithm to increase intelligibility for hearing-impaired listeners in reverberant competing talker conditions.

The Journal of the Acoustical Society of America Jun 1, 2020
Deep learning based speech separation or noise reduction needs to generalize to voices not encountered during training and to operate under multiple corruptions. The current study provides such a demonstration for hearing-impaired (HI) listeners. Sen...
Speech Perception Hearing Loss, Sensorineural Deep Learning Humans Algorithms Hearing Speech Intelligibility
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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
Algorithms Biomedical Research Risk Assessment Postoperative Complications Monitoring, Intraoperative Ultrasonography Anesthesia Humans Patient Admission Blood Transfusion Artificial Intelligence Intraoperative Complications Hospital Mortality Critical Care Machine Learning Intensive Care Units, Pediatric Heart Arrest Hemorrhage Airway Management Perioperative Care
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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.
Middle Aged Postoperative Complications Radiography Aged Male Knee Prosthesis Prosthesis Failure Humans Machine Learning Algorithms Female
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Artificial intelligence in the age of cognitive endoscopy.

Gastrointestinal endoscopy Jun 1, 2020
Artificial Intelligence Algorithms Endoscopy Humans Neoplasms Pilot Projects Deep Learning Cognition
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Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning.

Korean journal of radiology Jun 1, 2020
OBJECTIVE: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT.
Deep Learning Coronary Artery Disease Heart Ventricles Male Middle Aged Automation Humans Algorithms Retrospective Studies Female Tomography, X-Ray Computed Aged Sensitivity and Specificity
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