Titlebook: Artificial Intelligence in Medicine; 15th Conference on A John H. Holmes,Riccardo Bellazzi,Niels Peek Conference proceedings 2015 Springer

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Updating Stochastic Networks to Integrate Cross-Sectional and Longitudinal Studiese or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies on the other hand, are u

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Conceptual Modeling of Clinical Pathways: Making Data and Processes ConnectedIn this paper, we propose a framework for seamless conceptual modeling of both data and processes, and the seamless integration of temporalities for both clinical data and clinical tasks. Moreover, we apply our approach to model the clinical pathway for managing patients with ROSC (Return Of Spontaneous Circulation) in a real ICU clinical setting.

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Miguel Alonso,Alan Kramer,Javier Rodrigon to structured databases that store expert-curated information, unstructured and semi-structured data is a huge and often most up-to-date resource of medical knowledge. These include scientific literature, clinical narratives and social media, which typically capture findings, knowledge and experie

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Contingency, Choice and the Historian,physicians to annotate condition severity are time-consuming and costly. Previously, a passive learning algorithm called CAESAR was developed to capture severity in EHRs. This approach required physicians to label conditions manually, an exhaustive process. We developed a framework that uses two Act

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