Abstract
Several systems have been designed to reason about longitudinal patient data in terms of abstract, clinically meaningful concepts derived from raw time-stamped clinical data. However, current approaches are limited by their treatment of missing data and of the inherent uncertainty that typically underlie clinical raw data. Furthermore, most approaches have generally focused on a single patient. We have designed a new probability-oriented methodology to overcome these conceptual and computational limitations. The new method includes also a practical parallel computational model that is geared specifically for implementing our probabilistic approach in the case of abstraction of a large number of electronic medical records.
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© 2005 Springer-Verlag Berlin Heidelberg
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Ramati, M., Shahar, Y. (2005). Probabilistic Abstraction of Multiple Longitudinal Electronic Medical Records. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_6
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DOI: https://doi.org/10.1007/11527770_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
Online ISBN: 978-3-540-31884-2
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