What Is Missing in Surgical Risk Stratification and Outcome Prediction: A Scoping Review of End-to-End Machine Learning Approaches

• URL: http://arxiv.org/abs/2607.29090v1
• Date: Fri, 31 Jul 2026 07:16:50 GMT
• Title: What Is Missing in Surgical Risk Stratification and Outcome Prediction: A Scoping Review of End-to-End Machine Learning Approaches
• Abstract summary: Postoperative adverse events, including mortality and morbidity, remain a major global burden.<n>Machine learning (ML) provides a data-driven approach to model complex clinical patterns.<n>This scoping review characterizes ML pipelines for surgical risk stratification and outcome prediction using EHR data.
• Score: 8.57254488130299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Handling Missing Modalities in Multimodal Survival Prediction for Non-Small Cell Lung Cancer [28.475952006436227]
  We present a missing-aware multimodal survival framework that integrates Computed Tomography (CT), Whole-Slide Histopathology (WSI) Images, and structured clinical variables for overall survival modeling in unresectable stage II-III NSCLC.<n> Experimental results demonstrate that intermediate fusion consistently outperforms unimodal baselines as well as early and late fusion strategies.
  arXiv  Detail & Related papers  (2026-01-15T13:38:19Z)
• Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation [13.108896747775063]
  We introduce the Functional Competing Risk Net (FCRN), a unified deep-learning framework for discrete-time survival analysis under competing risks.<n>By combining a micro-network Basis Layer for functional data representation with a gradient-based imputation module, FCRN simultaneously learns to impute missing values and predict event-specific hazards.
  arXiv  Detail & Related papers  (2025-09-29T18:33:00Z)
• impuTMAE: Multi-modal Transformer with Masked Pre-training for Missing Modalities Imputation in Cancer Survival Prediction [75.43342771863837]
  We introduce impuTMAE, a novel transformer-based end-to-end approach with an efficient multimodal pre-training strategy.<n>It learns inter- and intra-modal interactions while simultaneously imputing missing modalities by reconstructing masked patches.<n>Our model is pre-trained on heterogeneous, incomplete data and fine-tuned for glioma survival prediction using TCGA-GBM/LGG and BraTS datasets.
  arXiv  Detail & Related papers  (2025-08-08T10:01:16Z)
• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• Machine Learning Applications in Medical Prognostics: A Comprehensive Review [0.0]
  Machine learning (ML) has revolutionized medical prognostics by integrating advanced algorithms with clinical data. RF models demonstrate robust performance in handling high-dimensional data. CNNs have shown exceptional accuracy in cancer detection. LSTM networks excel in analyzing temporal data, providing accurate predictions of clinical deterioration.
  arXiv  Detail & Related papers  (2024-08-05T09:41:34Z)
• Reviewing AI's Role in Non-Muscle-Invasive Bladder Cancer Recurrence Prediction [0.4369058206183195]
  Non-muscle-invasive Bladder Cancer (NMIBC) imposes a significant human burden and is one of the costliest cancers to manage.<n>Current tools for predicting NMIBC recurrence rely on scoring systems that often overestimate risk and have poor accuracy.<n>Machine learning (ML)-based techniques have emerged as a promising approach for predicting NMIBC recurrence by leveraging molecular and clinical data.
  arXiv  Detail & Related papers  (2024-03-15T17:03:45Z)
• ProtoKD: Learning from Extremely Scarce Data for Parasite Ova Recognition [5.224806515926022]
  We introduce ProtoKD, one of the first approaches to tackle the problem of multi-class parasitic ova recognition using extremely scarce data. We establish a new benchmark to drive research in this critical direction and validate that the proposed ProtoKD framework achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-09-18T23:49:04Z)
• Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing [62.9062883851246]
  Machine learning holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Using multi-task learning, we propose the first method to assess and mitigate shortcut learning as a part of the fairness assessment of clinical ML systems.
  arXiv  Detail & Related papers  (2022-07-21T09:35:38Z)
• Dissecting Self-Supervised Learning Methods for Surgical Computer Vision [51.370873913181605]
  Self-Supervised Learning (SSL) methods have begun to gain traction in the general computer vision community. The effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection.
  arXiv  Detail & Related papers  (2022-07-01T14:17:11Z)
• Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases [57.90226879210227]
  FedCy is a semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases.
  arXiv  Detail & Related papers  (2022-03-14T17:44:53Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.