Careflow Planning: From Time-Annotated Clinical Guidelines to Temporal Hierarchical Task Networks

Abstract

Abstract. 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 computeraided 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.

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

“…Temporal data mining has continued to receive attention together with temporal constraints and temporal knowledge retrieval [79][80][81]. Several papers have applied time-related research methods to deal with different clinical domains and tasks such as prognosis in the intensive care [82], querying and visualization of clinical abstractions [83] and guidelines-based care [84]. Finally, in 2013 the theme of temporal information management played a smaller role in AIME, and the main interest shifted towards more specific topics such as temporal data mining [85], rule derivation in surveillance systems [86] and care trajectory mining [87].…”