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

Sánchez-Garzón, Inmaculada; Fdez-Olivares, Juan; Castillo, Luis

doi:10.1007/978-3-642-36438-9_12

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Rules were used for a variety of medical tasks. Firstly, rules were used to identify potential contradictions in the personalization of clinical processes due to patient's preferences or comorbidities [ [53.59]], or to represent and manage medical exceptions that may occur during the enactment of a patient-centered care pathway [66]. Another application of rules was to help automate the process of modelling a clinical guideline [68,25].…”

Section: Decision Tables and Rulesmentioning

confidence: 99%

“…The most highly used medical vocabularies are UMLS [33,41,47,53,62,66,68,25], SNOMED-CT [34,22,62,72,74,93], MeSH [100], and the International Classification of Primary Care (ICPC) [34]. These vocabularies are used for mapping protocol elements [32,93] or computer interpretable guideline elements [19,46,24] to patient health records.…”

Section: Medical Informatics-specific Knowledge Representationsmentioning

confidence: 99%

“…iALARM, in [71], is an intelligent time-based alert language for activation, response, and monitoring of medical alerts. Paper [66] proposes temporal hierarchical planning techniques to handle exceptions arising during CIG enactment. In [82,91], it is acknowledged that the effects of the clinical actions have a probabilistic distribution in time, and that considering such probabilities further enhanced the support for interaction detection [93] represented temporal constraints in clinical guidelines using openEHR archetypes and Guideline Definition Language.…”

Section: Temporal Knowledge and Reasoning Knowledge-based Temporal Datmentioning

confidence: 99%

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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: Decision Tables and Rulesmentioning

confidence: 99%

Section: Medical Informatics-specific Knowledge Representationsmentioning

confidence: 99%

Section: Temporal Knowledge and Reasoning Knowledge-based Temporal Datmentioning

confidence: 99%

See 1 more Smart Citation

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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“…The work here presented adds adaptive execution capabilities for the care pathways. Another recent work [21] is focused on the medical exceptions representation and the differentiation of normal and exceptional flow in the formal guideline modeling.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-Driven Adaptive Execution of Care Pathways Based on Continuous Planning Techniques

Milla-Millán

Fdez-Olivares

Sánchez-Garzón

et al. 2013

Lecture Notes in Computer Science

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Abstract. Care pathways are excellent tools for the standardization of care delivery and the improvement of clinical efficiency. The high dynamism and unpredictability of the clinical environment require pathways to be adaptable to the deviations that may arise during their execution. In this work we present a methodology for the identification, monitoring, detection and managing of these deviations. Such deviations include evolving patient conditions, arbitrary medical modifications and unpredicted clinical settings. The care pathways are dynamically generated based on a knowledge-driven planning process that personalizes treatments according to up-to-date patient conditions and guarantees adherence to clinical guidelines recommendations. The implementation of the proposed methodology in a domain-independent continuous planning architecture is presented.Keywords: Personalization and adaptation of healthcare processes, Managing flexibility and exceptions in healthcare processes, Lifecycle management for healthcare processes, Process modeling in healthcare, Compliance of healthcare processes MotivationClinical guidelines and care pathways are clinical tools used in Clinical Decision Making in order to unify criteria according to the best scientific evidence [9,19]. Care Pathways can be seen as a way for adapting clinical guidelines to local conditions and their subsequent application to clinical practice [4]. These pathways are patient-centric processes that manage the patient care through the centralization of multidisciplinary tasks[13] which (1) are temporally annotated, (2)

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The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis

Jing¹

2021

JMIR Med Inform

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Background The Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications. Objective Despite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years. Methods PubMed, ACM Digital Library, and the Nursing & Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results A total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6%), information retrieval (125/975, 12.8%), terminology study (90/975, 9.2%), ontology and modeling (80/975, 8.2%), medical subdomains (76/975, 7.8%), other language studies (53/975, 5.4%), artificial intelligence tools and applications (46/975, 4.7%), patient care (35/975, 3.6%), data mining and knowledge discovery (25/975, 2.6%), medical education (20/975, 2.1%), degree-related theses (13/975, 1.3%), digital library (5/975, 0.5%), and the UMLS itself (150/975, 15.4%), as well as the UMLS for other purposes (27/975, 2.8%). Conclusions The UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions.

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

Cited by 4 publications

References 18 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

Knowledge-Driven Adaptive Execution of Care Pathways Based on Continuous Planning Techniques

The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis

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