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Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks.

, , , and . Artif. Intell. Medicine, 57 (2): 91-109 (2013)

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Identifying Disease-Centric Subdomains in Very Large Medical Ontologies: A Case-Study on Breast Cancer Concepts in SNOMED CT. Or: Finding 2500 Out of 300.000., , , , and . KR4HC, volume 5943 of Lecture Notes in Computer Science, page 50-63. Springer, (2009)Identifying Most Relevant Concepts to Describe Clinical Trial Eligibility Criteria., , , and . HEALTHINF, page 161-166. SciTePress, (2013)Patterns of Clinical Trial Eligibility Criteria., , , and . KR4HC, volume 6924 of Lecture Notes in Computer Science, page 145-157. Springer, (2011)Building a Library of Eligibility Criteria to Support Design of Clinical Trials., , and . EKAW, volume 7603 of Lecture Notes in Computer Science, page 327-336. Springer, (2012)Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks, , , and . Artificial Intelligence in Medicine, (2012)Careflow Planning: From Time-Annotated Clinical Guidelines to Temporal Hierarchical Task Networks., , , and . AIME, volume 6747 of Lecture Notes in Computer Science, page 265-275. Springer, (2011)Towards Automatic Patient Eligibility Assessment: From Free-Text Criteria to Queries., and . AIME, volume 7885 of Lecture Notes in Computer Science, page 78-83. Springer, (2013)Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks., , , and . Artif. Intell. Medicine, 57 (2): 91-109 (2013)Enhancing reuse of structured eligibility criteria and supporting their relaxation., , , , , and . J. Biomed. Informatics, (2015)