AIMC Topic: Workload
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Sustainable level of human performance with regard to actual availability in different professions.
Work (Reading, Mass.)
Jan 1, 2020
BACKGROUND: In a real working environment, workers' performance depends on the level of competence, psychological and health condition, motivation, and perceived stress. These are the attributes of actual availability. It is crucial to identify the m...
Putting the "why" in "EHR": capturing and coding clinical cognition.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2019
Complaints about electronic health records, including information overload, note bloat, and alert fatigue, are frequent topics of discussion. Despite substantial effort by researchers and industry, complaints continue noting serious adverse effects o...
EEG-Based Mental Workload Estimation.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Knowledge of the level of mental workload induced by any task is essential for optimizing load share among the operators. This helps in assessing their capability; besides, helping in task allocation. Since a persistently high workload experienced by...
Real-Time Cognitive Workload Monitoring Based on Machine Learning Using Physiological Signals in Rescue Missions.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
High levels of cognitive workload decreases human's performance and leads to failures with catastrophic outcomes in risky missions. Today, reliable cognitive workload detection presents a common major challenge, since the workload is not directly obs...
Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation.
Pharmaceutical medicine
Apr 1, 2019
INTRODUCTION: Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, and acting upon AEs. The volume of individual case safety reports (ICSRs) increases yearly, but it is...
AI peer reviewers unleashed to ease publishing grind.
Nature
Nov 1, 2018
Developing and maintaining clinical decision support using clinical knowledge and machine learning: the case of order sets.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2018
Development and maintenance of order sets is a knowledge-intensive task for off-the-shelf machine-learning algorithms alone. We hypothesize that integrating clinical knowledge with machine learning can facilitate effective development and maintenance...
Prediction of Nursing Workload in Hospital.
Studies in health technology and informatics
Jan 1, 2018
A dissertation project at the Witten/Herdecke University [1] is investigating which (nursing sensitive) patient characteristics are suitable for predicting a higher or lower degree of nursing workload. For this research project four predictive modell...
Feasibility of Automating Patient Acuity Measurement Using a Machine Learning Algorithm.
Journal of nursing measurement
Dec 1, 2016
BACKGROUND AND PURPOSE: One method of determining nurse staffing is to match patient demand for nursing care (patient acuity) with available nursing staff. This pilot study explored the feasibility of automating acuity measurement using a machine lea...
Day-to-day variability in hybrid, passive brain-computer interfaces: comparing two studies assessing cognitive workload.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Aug 1, 2016
As hybrid, passive brain-computer interface systems become more advanced, it is important to grow our understanding of how to produce generalizable pattern classifiers of physiological data. One of the most difficult problems in applying machine lear...
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