Conformance Analysis of the Execution of Clinical Guidelines with Basic Medical Knowledge and Clinical Terminology

Spiotta, Matteo; Bottrighi, Alessio; Giordano, Laura; Dupré, Daniele Theseider

doi:10.1007/978-3-319-13281-5_5

Cited by 7 publications

(9 citation statements)

References 11 publications

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“…Both "do" knowledge and the one that suggests avoiding or delaying an action ("do not" knowledge for short) refine rules proposed in [2], taking into account the temporal dimension.…”

Section: The Framework and An Example Casementioning

confidence: 96%

“…Conformance analysis is performed in Answer Set Programming, similarly to [2]; the results are ways to interpret a log according to the CG model and its possible alterations described above, with a minimum number of non-conformances.…”

Section: Fig 3 Discrepancies and Their Justificationsmentioning

confidence: 99%

“…In this paper, we extend [2] focusing on the temporal interplay between CGs and BMK from the viewpoint of a posteriori conformance analysis [3], intended as the adherence of an observed CG execution trace to both the CG and BMK. We do not provide any form of evaluation of how the interplay has been managed (i.e., whether the treatment was appropriate or not): we aim at identifying, in the trace, situations in which some recommendation (either in the CG or in the BMK) has not been followed, and at providing potential justifications for non-conformance to one knowledge source based on another source.…”

Section: Introductionmentioning

confidence: 99%

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Temporal Conformance Analysis of Clinical Guidelines Execution

Spiotta

Terenziani

Dupré

2015

Artificial Intelligence in Medicine

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Abstract. Physicians often have to combine clinical guideline recommendations with their own basic medical knowledge to cope with specific patients in specific contexts. Both knowledge sources may include temporal constraints for the execution of actions. In this paper we approach the problem of compliance analysis with both sources of knowledge, pointing out discrepancies -including temporal ones -with respect to them, and where such discrepancies may be due to multiple and possibly conflicting recommendations.

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“…Both "do" knowledge and the one that suggests avoiding or delaying an action ("do not" knowledge for short) refine rules proposed in [2], taking into account the temporal dimension.…”

Section: The Framework and An Example Casementioning

confidence: 96%

Section: Fig 3 Discrepancies and Their Justificationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temporal Conformance Analysis of Clinical Guidelines Execution

Spiotta

Terenziani

Dupré

2015

Artificial Intelligence in Medicine

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“…In the second case, knowledge can be acquired with tool-assisted methods or with qualitative methods, which are based on interviews or focus groups. With machine learning, health care data can be transformed into probabilistic models such as decision trees [43], Bayesian networks [69], Markov models [45,75,98], or rules [50], among others, that allow to capture a wide variety of knowledge concerning, for example, best care processes [43,69], the interpretation of mammography images [28], the prediction of sepsis [45], fall detection [99], risk situations for patients with dementia [101], or treatment inefficiencies due to patients' lack of adherence [95]. ML involves also unsupervised methods that can be used to create medical taxonomies about the topics observed in online medical digital libraries [17] to help information retrieval by consumers.…”

Section: Knowledge Generationmentioning

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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“…(3) Concerning the integration between a CIG and the BMK, we have focused on a posteriori analysis of conformance, intended as the adherence of an observed execution trace to CIG and BMK knowledge [3]. We have devised a general framework.…”

mentioning

confidence: 99%