Predicting Do-Not-Resuscitate Decisions in Critically Ill Patients Through Using Multitask Learning: A Retrospective Study of the MIMIC-IV Database.
Timely identification of critically ill patients for do-not-resuscitate (DNR) decisions is crucial to support shared decision-making and ethical end-of-life care. This study developed an explainable multitask learning (MTL) model using the MIMIC-IV database to predict the DNR decision within 24 h. T...
| Publicado en: | Inquiry (00469580) Vol. 63; pp. 1 - 11 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
2/21/2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=191764446&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 191764446 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 2/21/2026 vid: 63 pid: 344 pub: Sage Publications Inc. artinfo: ui: 191764446 10.1177/00469580261420721 ppf: 1 ppct: 10 formats: tig: atl: Predicting Do-Not-Resuscitate Decisions in Critically Ill Patients Through Using Multitask Learning: A Retrospective Study of the MIMIC-IV Database. aug: au: Lin, Ming-Yen Yeh, Chuan-Feng Chao, Wen-Cheng affil: Feng Chia University, Taichung, Taiwan Taichung Veterans General Hospital, Taiwan National Chung Hsing University, Taichung, Taiwan su: Databases Do-not-resuscitate orders Prediction models Receiver operating characteristic curves Research funding Nursing assessment Decision making Catastrophic illness Retrospective studies Mann Whitney U Test Descriptive statistics Chi-squared test Longitudinal method Medical records Acquisition of data Intensive care units Length of stay in hospitals Data analysis software Critically ill patient psychology sug: subj: Databases Do-not-resuscitate orders Prediction models Receiver operating characteristic curves Research funding Nursing assessment Decision making Catastrophic illness Retrospective studies Mann Whitney U Test Descriptive statistics Chi-squared test Longitudinal method Medical records Acquisition of data Intensive care units Length of stay in hospitals Data analysis software Critically ill patient psychology keyword: critical care do-not-resuscitate explainable AI MIMIC-IV multitask learning ab: Timely identification of critically ill patients for do-not-resuscitate (DNR) decisions is crucial to support shared decision-making and ethical end-of-life care. This study developed an explainable multitask learning (MTL) model using the MIMIC-IV database to predict the DNR decision within 24 h. The model was trained on data from 7789 adult patients who were admitted to the intensive care units (ICUs) with a length of stay longer than 3 days, using features including clinical parameters and nursing assessments across a 72-h window. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration plots, and decision curve analysis. Interpretability was illustrated by SHapley Additive exPlanations (SHAP) plots and partial dependence plots (PDP). Error analysis was performed to explore the strengths and limitations of the established model. The MTL model achieved superior performance compared to single-task learning (AUROC: 0.798 vs 0.764). SHAP and PDP plots demonstrated that verbalization ability, ventilatory support, and muscle strength as key features. Error analysis identified a subgroup of patients with extreme ventilatory demand and muscle weakness who contributed to misclassification; excluding this subgroup improved AUROC from 0.792 to 0.827. We developed a DNR prediction model and demonstrated the feasibility of integrating an explainable model into ICU care for a nudge to consider the DNR-relevant issue. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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