ArticlePloS one2016
Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
97 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.BMC nephrology · 2022Pooled it
- Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.Langenbeck's archives of surgery · 2022Pooled it
- Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.Intensive care medicine · 2020Pooled it
- Hierarchical Poincaré analysis for anaesthesia monitoring.Journal of clinical monitoring and computing · 2020Trial
- Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study.Surgery · 2019Trial
- Identification of acute kidney injury after non-cardiac surgery by machine learning leveraging a large electronic health record database.Scientific reports · 2026Article
- Application of a machine learning model integrating T cell subsets and clinical markers in 28-Day mortality prediction of sepsis patients.Clinical and experimental medicine · 2026Article
- Prediction of Postoperative Vomiting Within 24 Hours Using Machine Learning With Large Language Model-Enhanced Interpretability: Development and Validation Study.JMIR medical informatics · 2026Article
- Radiomics-based IVIM-DWI for early noninvasive assessment of renal allograft dysfunction.Abdominal radiology (New York) · 2026Article
- A machine learning model for post-operative sepsis prediction in acute surgical patients: a multi-centre, prospective study.Scientific reports · 2026Article
- Machine learning-based classification of papillary thyroid carcinoma versus multinodular goiter using preoperative laboratory and cytology data.BMC endocrine disorders · 2026Article
- Precision perioperative AI: from signals, images, and records to applications in anesthesia-a narrative mini-review proposing an operational framework.Frontiers in medicine · 2026Review
- Identifying determinants of unmet healthcare needs in South Korea: a comparison of machine learning approaches for precision public health.BMC public health · 2025Article
- Prediction of acute kidney injury after noncardiac surgery in intensive care unit by machine learning.Annals of medicine and surgery (2012) · 2025Article
- Analysis and development of risk prediction models for chronic opioid use after surgery: a cohort study using the nationwide database.Korean journal of anesthesiology · 2025Article
- Article
- Prediction model for postoperative pulmonary complications after thoracoscopic surgery with machine learning algorithms and SHapley Additive exPlanations.Journal of thoracic disease · 2025Article
- Federated Learning for Predicting Major Postoperative Complications.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
- Data-Driven Approach to Development of a Risk Score for Periprosthetic Joint Infections in Total Joint Arthroplasty Using Electronic Health Records.The Journal of arthroplasty · 2025Article
- Multicenter Development and Validation of a Multimodal Deep Learning Model to Predict Moderate to Severe AKI.Clinical journal of the American Society of Nephrology : CJASN · 2025Article
37 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
objectiveTo compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury.
designRetrospective single center cohort study of adult surgical patients admitted between 2000 and 2010. PATIENTS: 50,318 adult patients undergoing major surgery. MEASUREMENTS: We evaluated the performance of logistic regression, generalized additive models, naïve Bayes and support vector machines for forecasting postoperative sepsis and acute kidney injury. We assessed the impact of feature reduction techniques on predictive performance. Model performance was determined using the area under the receiver operating characteristic curve, accuracy, and positive predicted value. The results were reported based on a 70/30 cross validation procedure where the data were randomly split into 70% used for training the model and the 30% for validation. MAIN
resultsThe areas under the receiver operating characteristic curve for different models ranged between 0.797 and 0.858 for acute kidney injury and between 0.757 and 0.909 for severe sepsis. Logistic regression, generalized additive model, and support vector machines had better performance compared to Naïve Bayes model. Generalized additive models additionally accounted for non-linearity of continuous clinical variables as depicted in their risk patterns plots. Reducing the input feature space with LASSO had minimal effect on prediction performance, while feature extraction using principal component analysis improved performance of the models.
conclusionsGeneralized additive models and support vector machines had good performance as risk prediction model for postoperative sepsis and AKI. Feature extraction using principal component analysis improved the predictive performance of all models.
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