{"slug": "what-is-missing-in-surgical-risk-stratification-and-outcome-prediction-a-scoping", "title": "What Is Missing in Surgical Risk Stratification and Outcome Prediction: A Scoping Review of End-to-End Machine Learning Approaches", "summary": "A scoping review of 190 studies on machine learning for surgical risk stratification and outcome prediction using electronic health records found that most relied on single-center private datasets, with limited data modalities and incomplete reporting of preprocessing steps, while only about one-third incorporated explainability methods. The review, published on arXiv, identifies methodological gaps that hinder reproducibility and generalizability, and calls for benchmark datasets and standardized evaluation protocols to support clinically robust ML tools for perioperative care.", "body_md": "arXiv:2607.29090v1 Announce Type: new\nAbstract: Postoperative adverse events, including mortality and morbidity, remain a major global burden, many of which are preventable through early identification of high-risk patients and targeted perioperative care. Accurate risk stratification is therefore essential. With the growing availability of large-scale electronic health records (EHRs), machine learning (ML) provides a data-driven approach to model complex clinical patterns. However, existing studies vary widely in design, and methodological practices remain fragmented. This scoping review characterizes ML pipelines for surgical risk stratification and outcome prediction using EHR data. We reviewed 190 studies covering the ML workflow, including data preprocessing, algorithm selection, model evaluation, and explainability. Most studies relied on single-center private datasets with limited data modalities, while the scarcity of open-access surgical datasets constrained reproducibility and generalizability. Reporting of key preprocessing steps, including missing data handling, feature selection, and class imbalance, was often incomplete. Conventional ML models and simple neural networks predominated, whereas deep learning and multimodal approaches remained uncommon. Benchmark datasets and standardized evaluation protocols were largely absent, hindering cross-study comparisons. Only about one-third of studies incorporated explainability methods. This review identifies methodological gaps limiting clinically robust postoperative ML tools and provides a structured reference to support more rigorous, reproducible, and clinically meaningful ML development for perioperative care.", "url": "https://wpnews.pro/news/what-is-missing-in-surgical-risk-stratification-and-outcome-prediction-a-scoping", "canonical_source": "https://www.machinebrief.com/news/what-is-missing-in-surgical-risk-stratification-and-outcome-boep", "published_at": "2026-08-03 04:00:00+00:00", "updated_at": "2026-08-03 05:32:12.374560+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "ai-ethics"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/what-is-missing-in-surgical-risk-stratification-and-outcome-prediction-a-scoping", "markdown": "https://wpnews.pro/news/what-is-missing-in-surgical-risk-stratification-and-outcome-prediction-a-scoping.md", "text": "https://wpnews.pro/news/what-is-missing-in-surgical-risk-stratification-and-outcome-prediction-a-scoping.txt", "jsonld": "https://wpnews.pro/news/what-is-missing-in-surgical-risk-stratification-and-outcome-prediction-a-scoping.jsonld"}}