Robotics & Machine Learning Daily News2024,Issue(Sep.30) :63-64.

Data on Delirium Reported by Diether Kramer and Colleagues (Machine learning-bas ed delirium prediction in surgical in-patients: a prospective validation study)

Robotics & Machine Learning Daily News2024,Issue(Sep.30) :63-64.

Data on Delirium Reported by Diether Kramer and Colleagues (Machine learning-bas ed delirium prediction in surgical in-patients: a prospective validation study)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Nervous System Diseases and Condi tions - Delirium is the subject of a report. According to news reporting out of Graz, Austria, by NewsRx editors, research stated, "Delirium is a syndrome that leads to severe complications in hospitalized patients, but is considered preven table in many cases. One of the biggest chAllenges is to identify patients at ri sk in a hectic clinical routine, as most screening tools cause additional worklo ad." Our news journalists obtained a quote from the research, "The aim of this study was to validate a machine learning (ML)-based delirium prediction tool on surgic al in-patients undergoing a systematic assessment of delirium. 738 in-patients o f a vascular surgery, a trauma surgery and an orthopedic surgery department were screened for delirium using the DOS scale twice a day over their hospital stay. Concurrently, delirium risk was predicted by the ML algorithm in real-time for All patients at admission and evening of admission. The prediction was performed automaticAlly based on existing EHR data and without any additional documentati on needed. 103 patients (14.0%) were screened positive for delirium using the DOS scale. Out of them, 85 (82.5%) were correctly identi fied by the ML algorithm. Specificity was slightly lower, detecting 463 (72.9% ) out of 635 patients without delirium. The AUROC of the algorithm was 0.883 (95 % CI, 0.8523-0.9147). In this prospective validation study, the im plemented machine-learning algorithm was able to detect patients with delirium i n surgical departments with high discriminative performance."

Key words

Graz/Austria/Europe/Algorithms/Cybor gs/Delirium/Emerging Technologies/Health and Medicine/Machine Learning/Ment al Health/Nervous System Diseases and Conditions/Neurobehavioral Manifestation s/Neurologic Manifestations/Risk and Prevention/Surgery

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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