A5017147727- Intensive Care Unit Cognitive Disorders
- Cardiac, Anesthesia and Surgical Outcomes
- Anesthesia and Sedative Agents
- Machine Learning in Healthcare
- Frailty in Older Adults
- Anesthesia and Neurotoxicity Research
Saiseikai Kumamoto Hospital
2023-2024
Background Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation real-world settings remain unclear and require a comparison with conventional practical applications. Objective The objective this study was to validate temporal generalizability decision tree ensemble sparse linear regression for delirium after surgery compared that traditional logistic model. Methods health record data patients...
Delirium is common in the emergency department, and once it develops, there a risk of self-extubation drains tubes, so critical to predict delirium before occurs. Machine learning was used create two prediction models this study: one for predicting occurrence after delirium. Each model showed high discriminative performance, indicating possibility selecting high-risk cases. Visualization predictors using Shapley additive explanation (SHAP), machine interpretability method, that were...
Delirium in hospitalized patients is a worldwide problem, causing burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML method) presents the results of predictions promotes guided decisions. This study focuses visualizing predictors delirium using ML implementing analysis clinical practice. Retrospective data 55,389 single acute care center Japan between December 2017 February 2022 were collected. Patients categorized into three...
<sec> <title>BACKGROUND</title> Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation real-world settings remain unclear and require a comparison with conventional practical applications. </sec> <title>OBJECTIVE</title> The objective this study was to validate temporal generalizability decision tree ensemble sparse linear regression for delirium after surgery compared that traditional logistic model....
<sec> <title>BACKGROUND</title> The occurrence of delirium in hospitalized patients is a worldwide problem, not only because the burden it places on healthcare professionals, but also its impact patient prognosis. Therefore, although there has been much research use machine learning to predict advance, are few cases where results have applied clinical practice. Explainable artificial intelligence (XAI) techniques being increasingly adopted recent years these models present AI predictions...