@inproceedings{cc67af5b0bcd41f6b607f4142f910216,
title = "Probabilistic abstraction of multiple longitudinal electronic medical records",
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.",
author = "Michael Ramati and Yuval Shahar",
year = "2005",
month = jan,
day = "1",
doi = "10.1007/11527770\_6",
language = "English",
isbn = "3540278311",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "43--47",
booktitle = "Artificial Intelligence in Medicine - 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Proceedings",
address = "Germany",
note = "10th Conference on Artificial Intelligence in Medicine, AIME 2005 ; Conference date: 23-07-2005 Through 27-07-2005",
}