The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to g...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
Electronic health record (EHR) data must be mapped to standard information models for interoperability and to support research across organizations. New information models are being developed and validated for data important to nursing, but a signifi...
OBJECTIVE: This paper explores the implications of artificial intelligence (AI) on the management of healthcare data and information and how AI technologies will affect the responsibilities and work of health information management (HIM) professional...
Available medical knowledge exceeds the organizing capacity of the human mind, yet medical education remains based on information acquisition and application. Complicating this information overload crisis among learners is the fact that physicians' s...
IEEE journal of biomedical and health informatics
Mar 16, 2017
Health analysis often involves prediction of multiple outcomes of mixed type. The existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multioutcome prediction. Our proposed framework...
Intelligent agents and healthcare have been intimately linked in the last years. The intrinsic complexity and diversity of care can be tackled with the flexibility, dynamics and reliability of multi-agent systems. The purpose of this review is to sho...
The present work is related to Web intelligence and more precisely to medical information foraging. We present here a novel approach based on agents technology for information foraging. An architecture is proposed, in which we distinguish two importa...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Nov 5, 2015
Biomedical ontologies play a vital role in healthcare information management, data integration, and decision support. Ontology quality assurance (OQA) is an indispensable part of the ontology engineering cycle. Most existing OQA methods are based on ...
OBJECTIVE: Terminologies and terminological systems have assumed important roles in many medical information processing environments, giving rise to the "big knowledge" challenge when terminological content comprises tens of thousands to millions of ...
Artificial intelligence (AI) holds promise for cardiovascular medicine but is limited by a lack of large, heterogeneous and granular data sets. Blockchain provides secure interoperability between siloed stakeholders and centralized data sources. We d...
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