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Medical Informatics

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Architecture and Initial Development of a Digital Library Platform for Computable Knowledge Objects for Health.

Studies in health technology and informatics
Throughout the world, biomedical knowledge is routinely generated and shared through primary and secondary scientific publications. However, there is too much latency between publication of knowledge and its routine use in practice. To address this l...

Health informatics to optimize complex laboratory developed test configurations.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Configuration of complex Laboratory Developed Tests (LDTs) is a time-consuming and complicated task, potentially leading to inconsistent LDTs in which features constraints remain unresolved and important features could remain unselected.

Visualizing patient journals by combining vital signs monitoring and natural language processing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admi...

Ontology-Oriented Programming for Biomedical Informatics.

Studies in health technology and informatics
Ontologies are now widely used in the biomedical domain. However, it is difficult to manipulate ontologies in a computer program and, consequently, it is not easy to integrate ontologies with databases or websites. Two main approaches have been propo...

Robotics in Medicine.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association

Health Informatics via Machine Learning for the Clinical Management of Patients.

Yearbook of medical informatics
OBJECTIVES: To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice.