Task Network Based Modeling, Dynamic Generation and Adaptive Execution of Patient-Tailored Treatment Plans Based on Smart Process Management Technologies

Fdez-Olivares, Juan; Inmaculada, S; nchez-Garzón,; Gonz, Arturo; lez-Ferrer,; Cózar, Juan A.; Fdez-Teijeiro, Ana; Cabello, Manuel R.; Castillo, Luis

doi:10.1007/978-3-642-27697-2_3

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Among the papers, there are some in which new engines for the enactment of CIGs are described. The main approaches focus on adaptive continuous planning [51,60], a meta-engine for CIG execution [80], and the use of semantic-web inference rules in SPARQL [86,87] for clinical guidelines application.…”

Section: Knowledge Enactmentmentioning

confidence: 99%

Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges

Riaño

Peleg

Teije

2019

Artificial Intelligence in Medicine

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Background: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide. Objectives: Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future. Methods: We reviewed the title, the abstract, and the keywords of the 112 papers that were accepted to the workshop, identified the medical and technological topics involved in these works, provided a classification of these papers in medical and technological perspectives and obtained the timeline of these topics in order to determine interest growths and declines. The experience of the authors in the field and the evidences after the review were the basis to propose a list of challenges of knowledge representation in health care for the future. Results: The most generic knowledge representation methods are ontologies (31%), semantic web related formalisms (26%), decision tables and rules (19%), logic (14%), and probabilistic models (10%). From a medical informatics perspective, knowledge is mainly represented as computer interpretable clinical guidelines (43%), medical domain ontologies (26%), and electronic health care records (22%). Within the knowledge lifecycle, contributions are found in knowledge generation (38%), knowledge specification (24%), exception detection and management (12%), knowledge enactment (8%), temporal knowledge and reasoning (7%), and knowledge sharing and maintenance (7%). The clinical emphasis of knowledge is mainly related to clinical treatments (27%), diagnosis (13%), clinical quality indicators (13%), and guideline integration for multimorbid patients (12%). According to the level of development of the works presented, we distinguished four maturity levels: formal (22%), implementation (52%), testing (13%), and deployment (2%) levels. Some papers described technologies for specific clinical issues or diseases, mainly cancer (22%) and diseases of the circulatory system (20%). Chronicity and comorbidity were present in 10% and 8% of the papers, respectively. Conclusions: KR4HC is a stable community, still active after ten years. A persistent focus has been knowledge representation, with an emphasis on semantic-web ontologies and on clinical-guideline based decision-support. Among others, two topics receive growing attention: integration of computer-interpretable guideline knowledge for the management of multimorbidity patients, and patie...

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Section: Knowledge Enactmentmentioning

confidence: 99%

Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges

Riaño

Peleg

Teije

2019

Artificial Intelligence in Medicine

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“…In this regard, work is currently in progress [33,60] aimed at increasing the flexibility of our approach, so that it can be used in more complex and highly dynamic scenarios. This involves considering exception handling, by including a monitoring engine that detects inconsistent situations and that triggers either a plan repair or replanning step in order to adapt to the new situation.…”

Section: Related Workmentioning

confidence: 99%

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

González-Ferrer¹,

Teije²,

Fdez-Olivares³

et al. 2013

Artificial Intelligence in Medicine

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Objective: This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To this end, a translation from a time-annotated computerinterpretable guideline (CIG) model of a clinical protocol into a temporal hierarchical task network (HTN) planning domain is presented.Methods and Material: The proposed method uses a knowledge-driven reasoning process to translate knowledge previously described in a CIG into a corresponding HTN Planning and Scheduling domain, taking advantage of HTNs known ability to i) dynamically cope with temporal and resource constraints, and ii) automatically generate customized plans. The proposed method, focusing on the representation of temporal knowledge and based on the identification of workflow and temporal patterns in a CIG, makes it possible to automatically generate time-annotated and resource-based care pathways tailored to the needs of any possible patient profile.Results: The proposed translation is illustrated through a case study based on a 70 pages long clinical protocol to manage Hodgkin's disease, developed by the Spanish Society of Pediatric Oncology. We show that an HTN planning domain can be generated from the corresponding specification of the protocol in the Asbru language, providing a running example of this translation. Furthermore, the correctness of the translation is checked and also the management of ten di↵erent types of temporal patterns represented in the protocol. By interpreting the automatically generated domain with a state-of-art HTN planner, a time-annotated care pathway is automatically obtained, customized for the patient's and institutional needs. The generated care pathway can then be used by clinicians to plan and manage the patients long-term care.Conclusion: The described methodology makes it possible to automatically generate patient-tailored care pathways, leveraging an incremental knowledge-driven engineering process that starts from the expert knowledge of medical professionals. The presented approach makes the most of the strengths inherent in both CIG languages and HTN planning and scheduling techniques: for the former, knowledge acquisition and representation of the original clinical protocol, and for the latter, knowledge reasoning capabilities and an ability to deal with complex temporal and resource constraints. Moreover, the proposed approach provides immediate access to technologies such as business process management (BPM) tools, which are increasingly being used to support healthcare processes.

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An Approach for Representing and Managing Medical Exceptions in Care Pathways Based on Temporal Hierarchical Planning Techniques

Sánchez-Garzón

Fdez-Olivares

Castillo

2013

Lecture Notes in Computer Science

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Task Network Based Modeling, Dynamic Generation and Adaptive Execution of Patient-Tailored Treatment Plans Based on Smart Process Management Technologies

Cited by 4 publications

References 7 publications

Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges

Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges

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

An Approach for Representing and Managing Medical Exceptions in Care Pathways Based on Temporal Hierarchical Planning Techniques

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