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Development and Validation of Models for Preoperative Prediction of Risk and Postoperative Detection of Noninfectious Complications Using Interpretable Machine Learning and Electronic Health Record Data.

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Title: Development and Validation of Models for Preoperative Prediction of Risk and Postoperative Detection of Noninfectious Complications Using Interpretable Machine Learning and Electronic Health Record Data.
Authors: Dyas AR; Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO., Stuart CM; Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO., Fei Y; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO., Meguid RA; Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO.; Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO., Zhuang Y; Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.; Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO., Henderson WG; Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO., Bronsert MR; Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO.; Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO., Colborn KL; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO.; Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.; Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO.
Source: Annals of surgery [Ann Surg] 2026 Aug 01; Vol. 284 (2), pp. 273-280. Date of Electronic Publication: 2025 Mar 26.
Publication Type: Journal Article; Validation Study; Multicenter Study
Journal Info: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 0372354 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1528-1140 (Electronic) Linking ISSN: 00034932 NLM ISO Abbreviation: Ann Surg Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Development and Validation of Models for Preoperative Prediction of Risk and Postoperative Detection of Noninfectious Complications Using Interpretable Machine Learning and Electronic Health Record Data.
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  Data: <searchLink fieldCode="AU" term="%22Dyas+AR%22">Dyas AR</searchLink>; Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO.<br /><searchLink fieldCode="AU" term="%22Stuart+CM%22">Stuart CM</searchLink>; Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO.<br /><searchLink fieldCode="AU" term="%22Fei+Y%22">Fei Y</searchLink>; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO.<br /><searchLink fieldCode="AU" term="%22Meguid+RA%22">Meguid RA</searchLink>; Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO.; Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO.<br /><searchLink fieldCode="AU" term="%22Zhuang+Y%22">Zhuang Y</searchLink>; Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.; Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO.<br /><searchLink fieldCode="AU" term="%22Henderson+WG%22">Henderson WG</searchLink>; Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO.<br /><searchLink fieldCode="AU" term="%22Bronsert+MR%22">Bronsert MR</searchLink>; Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus, Aurora, CO.; Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO.<br /><searchLink fieldCode="AU" term="%22Colborn+KL%22">Colborn KL</searchLink>; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO.; Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.; Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Lippincott+Williams+%26+Wilkins%22">Lippincott Williams & Wilkins </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>0372354 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1528-1140 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200034932%22">00034932 </searchLink><i>NLM ISO Abbreviation: </i>Ann Surg <i>Subsets: </i>MEDLINE
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        Value: 10.1097/SLA.0000000000006709
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        Text: English
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        StartPage: 273
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      – TitleFull: Development and Validation of Models for Preoperative Prediction of Risk and Postoperative Detection of Noninfectious Complications Using Interpretable Machine Learning and Electronic Health Record Data.
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              M: 08
              Text: 2026 Aug 01
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