Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database

Machine learning models can predict pediatric SSI risk using preoperative data. Our regularized logistic regression model is a promising candidate for integration into electronic health record systems to enable individualized risk estimation and targeted infection prevention. Next steps include external validation and user-centered implementation studies.

Authors

Chung Yip Chan (ORCID: https://orcid.org/0000-0002-9397-0908)
Mark J. Pletcher (ORCID: https://orcid.org/0000-0002-6966-1312)
Karthik Balakrishnan (ORCID: https://orcid.org/0000-0002-0244-249X)
Yulin Hswen (ORCID: https://orcid.org/0000-0003-3203-1322)

Institutions

University of California, San Francisco (US)
Children's Health Council (US)
Stanford Medicine (US)

Publication Details

Journal: Journal of the American College of Surgeons
Published: 2025-11-03
DOI: https://doi.org/10.1097/xcs.0000000000001683
Primary Topic: Surgical site infection prevention
Type: article
Field-Weighted Citation Impact: 0.00

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article

Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database

Chung Yip Chan, Mark J. Pletcher, Karthik Balakrishnan, Yulin Hswen

Nov 3, 2025

Journal of the American College of Surgeons

Surgical site infection prevention

article

Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database

Chung Yip Chan, Mark J. Pletcher, Karthik Balakrishnan, Yulin Hswen

November 3, 2025article en

Abstract

Machine learning models can predict pediatric SSI risk using preoperative data. Our regularized logistic regression model is a promising candidate for integration into electronic health record systems to enable individualized risk estimation and targeted infection prevention. Next steps include external validation and user-centered implementation studies.

Journal of the American College of SurgeonsVol. 242(3)

University of California, San Francisco (US), Children's Health Council (US), Stanford Medicine (US)

Openalex Percentile: Top 50%

Surgical site infection prevention

DOI Open Access PDF OpenAlex

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Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database — Chung Yip Chan, Mark J. Pletcher, et al. · Journal of the American College of Surgeons (2025) | TGRS Research Map | TGRS