April 30, 2024, 4:42 a.m. | Annie Hu, Samuel Stockman, Xun Wu, Richard Wood, Bangdong Zhi, Oliver Y. Ch\'en

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.18670v1 Announce Type: new
Abstract: Early and timely prediction of patient care demand not only affects effective resource allocation but also influences clinical decision-making as well as patient experience. Accurately predicting patient care demand, however, is a ubiquitous challenge for hospitals across the world due, in part, to the demand's time-varying temporal variability, and, in part, to the difficulty in modelling trends in advance. To address this issue, here, we develop two methods, a relatively simple time-vary linear model, and …

abstract advanced arxiv challenge clinical cs.lg decision demand experience hospitals however machine machine learning making part patient patient care prediction simple stat.ap type uncertain world

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US