arXiv AI June 17, 2026 By Suleman Baloch, Kishlay Jha, Alberto M. Segre, Philip M. Polgreen, Bijaya Adhikari

A Machine-Learned Comorbidity Index

arXiv:2606. 17450v1 Announce Type: new Abstract: Traditional comorbidity scores (e.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

Aug 3

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

arXiv:2607. 29090v1 Announce Type: new 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.

By Yizhi Dong, Yuhe Ke, Hairil Rizal Abdullah, Yucheng Xing, Kevan Kai Bing Teo, Ling Huang, Mengling Feng

multimodal benchmarks

arXiv Machine Learning

Aug 4

A reproducible and extensible framework for benchmarking competing risks survival models

arXiv:2608. 00271v1 Announce Type: cross Abstract: A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.

By Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Sarah Friedrich-Welz, Catalina A. Vallejos

benchmarks safety

arXiv Machine Learning

Aug 11

A Domain-Structured Ensemble Framework for Perioperative Outcome Prediction Using Electronic Health Record Data

arXiv:2608. 08920v1 Announce Type: new Abstract: Perioperative risk prediction models are often limited by narrow surgical populations, incomplete intraoperative data, poor calibration, and limited interpretability.

By Shikhar Shukla, Cristina Barboi

benchmarks safety

arXiv AI

Jun 2

Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints

arXiv:2604. 01841v2 Announce Type: replace Abstract: Clinical prediction from structured electronic health records (EHRs) is challenging due to high dimensionality, heterogeneity, class imbalance, and distribution shift.

By Minh-Khoi Pham, Thang-Long Nguyen Ho, Thao Thi Phuong Dao, Tai Tan Mai, Minh-Triet Tran, Marie E. Ward, Una Geary, Rob Brennan, Nick McDonald, Martin Crane, Marija Bezbradica

llms rag benchmarks safety

arXiv AI

Jul 22

Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation

arXiv:2607. 18270v1 Announce Type: new Abstract: While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge.

By Kyunghoon Jeon, Youmin Ko, Woohwan Jung, Hyunjoon Kim

rag efficiency benchmarks

arXiv Machine Learning

Jun 10

Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports

arXiv:2606. 10725v1 Announce Type: new Abstract: Background.

By Olga Shakhmatova, Dmitrii Kriukov, Daniil Larionov, Nikita Khromov, Iaroslav Bespalov, Alexander Zolotarev, Kirill Grishchenkov, Ekaterina Ivanova, Miron Kuznetsov, Ilya Sochenkov, Elizaveta Panchenko, Artem Shelmanov, Dmitry V. Dylov

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Related stories

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

A reproducible and extensible framework for benchmarking competing risks survival models

A Domain-Structured Ensemble Framework for Perioperative Outcome Prediction Using Electronic Health Record Data

Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints

Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation

Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports