HTN planning: complexity and expressivity
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Artificial Intelligence in Medicine
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
A Holistic Environment for the Design and Execution of Self-Adaptive Clinical Pathways
IEEE Transactions on Information Technology in Biomedicine
Guideline-based careflow systems
Artificial Intelligence in Medicine
KR4HC'11 Proceedings of the 3rd international conference on Knowledge Representation for Health-Care
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Decision-making, care planning and adaptation of treatment are important aspects of the work of clinicians, that can clearly benefit from IT support. Clinical Practice Guidelines (CPG) languages provide formalisms for specifying knowledge related to such tasks, such as decision criteria and time-oriented aspects of the patient treatment. In these CPG languages, little research has been directed to efficiently deal with the integration of temporal and resource constraints, for the purpose of generating patient tailored treatment plans, i.e. care pathways. This paper presents an AI-based knowledge engineering methodology to develop, model, and operationalize care pathways, providing computer-aided support for the planning, visualization and execution of the patient treatment. This is achieved by translating time-annotated Asbru CPG's into temporal HTN planning domains. The proposed methodology is illustrated through a case study based on Hodgkin's disease.