AIMC Topic: Workload
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Performance and usability of machine learning for screening in systematic reviews: a comparative evaluation of three tools.
Systematic reviews
Nov 15, 2019
BACKGROUND: We explored the performance of three machine learning tools designed to facilitate title and abstract screening in systematic reviews (SRs) when used to (a) eliminate irrelevant records (automated simulation) and (b) complement the work o...
Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections.
BMC medical informatics and decision making
Aug 23, 2019
BACKGROUND: A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield negative culture results. By reducing the ...
Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy.
International journal of radiation oncology, biology, physics
Aug 1, 2019
PURPOSE: To assess the accuracy of machine learning to predict and classify quality assurance (QA) results for volumetric modulated arc therapy (VMAT) plans.
Data Accuracy
Quality Assurance, Health Care
Genital Neoplasms, Female
Radiotherapy, Intensity-Modulated
Female
Reproducibility of Results
Sensitivity and Specificity
Head and Neck Neoplasms
Poisson Distribution
Regression Analysis
Workload
Radiotherapy Planning, Computer-Assisted
Humans
Machine Learning
On-field player workload exposure and knee injury risk monitoring via deep learning.
Journal of biomechanics
Jul 8, 2019
In sports analytics, an understanding of accurate on-field 3D knee joint moments (KJM) could provide an early warning system for athlete workload exposure and knee injury risk. Traditionally, this analysis has relied on captive laboratory force plate...
Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 26, 2019
People's mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious accidents caused by excessive mental workload. Both electroencephalogram (EEG) spectral features and its...
Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.
European radiology
Apr 16, 2019
PURPOSE: To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload.
Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error.
Systematic reviews
Jan 15, 2019
BACKGROUND: Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm ...
Clinical text annotation - what factors are associated with the cost of time?
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
Building high-quality annotated clinical corpora is necessary for developing statistical Natural Language Processing (NLP) models to unlock information embedded in clinical text, but it is also time consuming and expensive. Consequently, it important...
Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Dec 3, 2018
Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for extending a pre-trained model to new workload ...
Machine learning to anticipate delivery room activity?
Journal of gynecology obstetrics and human reproduction
Dec 1, 2018
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