首页|Ulm University Reports Findings in Delirium [Introducing a ma chine learning algorithm for delirium prediction-the Supporting SURgery with GEr iatric Co-Management and AI project (SURGE-Ahead)]
Ulm University Reports Findings in Delirium [Introducing a ma chine learning algorithm for delirium prediction-the Supporting SURgery with GEr iatric Co-Management and AI project (SURGE-Ahead)]
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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 origina ting from Ulm, Germany, by NewsRx correspondents, research stated, “Post-operati ve delirium (POD) is a common complication in older patients, with an incidence of 14-56 %. To implement preventative procedures, it is necessary to identify patients at risk for POD.” Financial support for this research came from German Federal Ministry of Educati on and Research. Our news editors obtained a quote from the research from Ulm University, “In the present study, we aimed to develop a machine learning (ML) model for POD predic tion in older patients, in close cooperation with the PAWEL (patient safety, cos t-effectiveness and quality of life in elective surgery) project. The model was trained on the PAWEL study’s dataset of 878 patients (no intervention, age 70, 2 09 with POD). Presence of POD was determined by the Confusion Assessment Method and a chart review. We selected 15 features based on domain knowledge, ethical c onsiderations and a recursive feature elimination. A logistic regression and a l inear support vector machine (SVM) were trained, and evaluated using receiver op erator characteristics (ROC). The selected features were American Society of Ane sthesiologists score, multimorbidity, cut-to-suture time, estimated glomerular f iltration rate, polypharmacy, use of cardio-pulmonary bypass, the Montreal cogni tive assessment subscores ‘memory’, ‘orientation’ and ‘verbal fluency’, pre-exis ting dementia, clinical frailty scale, age, recent falls, post-operative isolati on and pre-operative benzodiazepines. The linear SVM performed best, with an ROC area under the curve of 0.82 [95% CI 0.78-0.85 ] in the training set, 0.81 [95% CI 0.71-0.88] in the test set and 0.76 [95 % CI 0.71-0.79] in a cross-centre validation. W e present a clinically useful and explainable ML model for POD prediction.” According to the news editors, the research concluded: “The model will be deploy ed in the Supporting SURgery with GEriatric Co-Management and AI project.”
UlmGermanyEuropeAlgorithmsCyborg sDeliriumEmerging TechnologiesHealth and MedicineMachine LearningMenta l HealthNervous System Diseases and ConditionsNeurobehavioral ManifestationsNeurologic ManifestationsSurgery
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