@inProceedings{
 title = {Analyzing Differences in Operational Disease Definitions using Ontological Modeling with an Application in Severe Sepsis},
 type = {inProceedings},
 year = {2007},
 pages = {268-275},
 city = {Utrecht, the Netherlands},
 editors = {[object Object],[object Object]},
 id = {aec2db0a-34c2-3c11-8ff6-ecd5e24a9eaf},
 created = {2015-05-16T16:30:52.000Z},
 file_attached = {false},
 profile_id = {0c4c49d2-3b4e-3d8d-9ea8-7bab2d49bb47},
 group_id = {7fc8fad4-ddf6-3b48-b39c-c05d75ba7135},
 last_modified = {2017-03-14T10:16:57.191Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {DBLP:conf/aime/PeelenKSKP07},
 source_type = {inproceedings},
 abstract = {In medicine, many diseases cannot be defined unequivocally by etiology or anatomical localization, but are instead described by a combination of signs and symptoms that are common in patients believed to be having that disease. In communication between health care professionals, this is generally not problematic. In biomedical research, however, crisp definitions are required to distinguish patients with and without the disease. This results in different operational definitions being in use for a single disease. Comparing those definitions, e.g, for trial design, is complicated by their complex structure. This paper presents an approach to compare different operational definitions of a single disease in a systematic manner using formal ontological modeling and reasoning. The concept of an operationalization hierarchy is introduced, which is subsequently used to model and analyze operational disease definitions. The approach is illustrated with a case-study in the area of severe sepsis.},
 bibtype = {inProceedings},
 author = {Peelen, Linda and Klein, Michel and Schlobach, Stefan and de Keizer, Nicolette and Peek, Niels},
 booktitle = {Proceedings of the 19th Belgian-Dutch Conference on Artificial Intelligence (BNAIC'07)}
}

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