arXiv Machine Learning

A Bayesian Learning Approach for Drone Coverage Network: A Case Study on Cardiac Arrest in Scotland

arXiv AI
Aug 6

Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction

arXiv:2605. 24212v2 Announce Type: replace-cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain.

By Siqi Li, Chuan Hong, Ziye Tian, Benjamin Sieu-Hon Leong, Koshi Nakagawa, Hideharu Tanaka, Sang Do Shin, Khuong Quoc Dai, Do Ngoc Son, Marcus Eng Hock Ong, Nan Liu, Molei Liu
Hugging Face Trending Papers
Sep 8

A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes

The paper presents a four‑module, data‑driven framework to identify and prioritize robotic process automation (RPA) opportunities in U.S. hospitals. It includes a process taxonomy, an automation suitability index, a tool‑tier selection recommendation, and a return‑on‑investment analysis, all applied to a synthetic portfolio of twenty hospital processes. The authors demonstrate the framework’s robustness through Monte Carlo simulations and discuss governance and future validation steps.

arXiv AI
Jun 16

Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget

arXiv:2509. 00135v2 Announce Type: replace Abstract: As part of nationwide efforts aligned with the United Nations' Sustainable Development Goal 3 on Universal Health Coverage, Ethiopia's Ministry of Health is strengthening health posts to expand access to essential healthcare services.

By Davin Choo, Yohai Trabelsi, Fentabil Getnet, Samson Warkaye Lamma, Wondesen Nigatu, Kasahun Sime, Lisa Matay, Milind Tambe, St\'ephane Verguet
arXiv AI
Jun 24

EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting

arXiv:2512. 15067v4 Announce Type: replace-cross Abstract: The rapid growth in wireless infrastructure has increased the need to accurately estimate and forecast electromagnetic field (EMF) levels to ensure ongoing compliance, assess potential health impacts, and support efficient network planning.

By Zijiang Yan, Yixiang Huang, Jianhua Pei, Hina Tabassum, Luca Chiaraviglio