March 18, 2024, 4:47 a.m. | Rasoul Samani, Fahime Shahrokh, Mohammad Dehghani

cs.CL updates on arXiv.org arxiv.org

arXiv:2403.09722v1 Announce Type: new
Abstract: Today, the existence of a vast amount of electronic health data has created potential capacities for conducting studies aiming to improve the medical services provided to patients and reduce the costs of the healthcare system. One of the topics that has been receiving attention in the field of medicine in recent years is the identification of patients who are likely to be re-hospitalized shortly after being discharged from the hospital. This identification can help doctors …

abstract arxiv biomedical clinical concepts costs cs.ai cs.cl data electronic health healthcare healthcare system health data medical patients prediction reduce services studies topics type vast

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

AI Engineering Manager

@ M47 Labs | Barcelona, Catalunya [Cataluña], Spain