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. All approaches had to some degree severe limitations in their treatment of incompleteness and uncertainty that typically underlie the raw data, on which the temporal reasoning is performed, and have generally narrowed their interest to a single subject. 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.
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.
| Original language | English |
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| Title of host publication | Edit Notes of the 9th International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2005) |
| Number of pages | 6 |
| State | Published - 24 Jul 2005 |