ArticleScientific reports2023
Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Dynamic Prediction of Clinical Endpoints in Survival Analysis With Machine and Deep Learning: A Systematic Review and Regulatory Perspective.CPT: pharmacometrics & systems pharmacology · 2026Pooled it
- Toward precision hernia surgery: integrating biomarkers, genomics, quantitative imaging, and artificial intelligence in abdominal wall repair.Hernia : the journal of hernias and abdominal wall surgery · 2026Review
- Posterior Communicating Artery Aneurysm Microsurgery: PComA-CORE, an Anatomy-Informed Explainable AI Framework for Complexity, Neurovascular Risk, Oculomotor Recovery and Functional Outcome.Medical sciences (Basel, Switzerland) · 2026Article
- Review
- From Static Risk Estimates to Dynamic Clinical Trajectories: A Conceptual Analysis of Perioperative Medicine.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- End-of-surgery prediction of postoperative infectious complications from intraoperative vital-sign dynamics.NPJ digital medicine · 2026Article
- Review
- Preliminary Usability Assessment of a Rule-Based Digital Self-Monitoring Platform for Patients With Brain Tumors Toward Digital Early Warning Systems: Pilot Feasibility Study.JMIR formative research · 2026Article
- DSPONVNet: a multimodal deep learning model integrating intraoperative monitoring and clinical features for predicting postoperative nausea and vomiting risk.BMC medical research methodology · 2026Article
- MELD score is predictive of postoperative morbidity and mortality in patients with acute appendicitis undergoing appendectomy: ACS-NSQIP data analysis.Surgical endoscopy · 2026Article
- Artificial intelligence in surgical decision-making across the perioperative continuum: a scoping review.Frontiers in digital health · 2026Review
- Risk stratification for postoperative complications after CRS and HIPEC in recurrent ovarian cancer patients: a comparative analysis of logistic regression and machine learning models.Scientific reports · 2025Article
- A narrative review on artificial intelligence in neurosurgery: ethical challenges and implementation considerations.Annals of medicine and surgery (2012) · 2025Review
- From prediction to clinical action in colorectal surgery: rethinking DLNN use for postoperative risk.Techniques in coloproctology · 2025Article
- Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.Patient safety in surgery · 2025Review
- The complex task of modelling artificial intelligence workflows for forecasting postoperative risk.Journal of anesthesia, analgesia and critical care · 2025Article
- From conventional scores to explainable AI: a six-method comparative framework for failure prediction in percutaneous nephrolithotomy.World journal of urology · 2025Article
- Prediction of postoperative infections by strategic data imputation and explainable machine learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- DiabCompSepsAI: Integrated AI Model for Early Detection and Prediction of Postoperative Complications in Diabetic Patients-Using a Random Forest Classifier.Journal of clinical medicine · 2025Article
- Machine-learning prediction of postoperative complications after high tibial osteotomy for canine cranial cruciate ligament disease.Veterinary surgery : VS · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform conventional machine learning models in predicting postoperative complications, and that integrating high-resolution intraoperative physiological time series would result in more granular and personalized health representations that would improve prognostication compared to preoperative predictions. In a longitudinal cohort study of 56,242 patients undergoing 67,481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests and XGBoost for predicting nine common postoperative complications using preoperative, intraoperative, and perioperative patient data. Our study indicated several significant results across experimental settings that suggest the utility of deep learning for capturing more precise representations of patient health for augmented surgical decision support. Multi-task learning improved efficiency by reducing computational resources without compromising predictive performance. Integrated gradients interpretability mechanisms identified potentially modifiable risk factors for each complication. Monte Carlo dropout methods provided a quantitative measure of prediction uncertainty that has the potential to enhance clinical trust. Multi-task learning, interpretability mechanisms, and uncertainty metrics demonstrated potential to facilitate effective clinical implementation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.