Model-Based Combination of Treatments for the Management of Chronic Comorbid Patients

Riaño, David; Collado, Antoni

doi:10.1007/978-3-642-38326-7_2

Cited by 25 publications

(19 citation statements)

References 7 publications

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“…To the best of our knowledge, no other approach in the literature has explicitly addressed such a challenging goal. In this sense, we believe that our approach, besides being innovative, is somehow complementary in respect of several other approaches in the literature, so that an integration with them can be a possible area of future work (e.g., with Riaño and Collado (2013) methodology to merge CIGs).…”

Section: Discussionmentioning

confidence: 90%

“…This sets up the urgent need of developing ways of merging multiple single-disease interventions to provide professionals' assistance to comorbid patients". (Riaño and Collado 2013).…”

Section: A Critical Issue: the Treatment Of Comorbid Patientsmentioning

confidence: 99%

“…Notably, in the last Artificial Intelligence in Medicine Europe (AIME'13) Conference, held in Murcia, Spain, from 29/5/2013 to 1/6/2013, a whole session (4 papers) was devoted to such an issue (Sánchez-Garzón et al 2013) (Jafarpour and Abidi 2013) (Riaño and Collado 2013) . The state of the art in the computer-based treatment of co-morbidities is presented in section "Related Works".…”

Section: A Critical Issue: the Treatment Of Comorbid Patientsmentioning

confidence: 99%

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An ontological knowledge and multiple abstraction level decision support system in healthcare

2014

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The rationalization of the healthcare processes and organizations is a task of fundamental importance to grant both the quality and the standardization of healthcare services, and the minimization of costs. Clinical Practice Guidelines (CPGs) are one of the major tools that have been introduced to achieve such a challenging task. CPGs are widely used to provide decision support to physicians, supplying them with evidence-based predictive and prescriptive information about patients' status and treatments, but usually on individual pathologies. This sets up the urgent need for developing decision support methodologies to assist physicians and healthcare managers in the detection of interactions between guidelines, to help them to devise appropriate patterns of treatment for comorbid patients (i.e., patients affected by multiple diseases). We identify different levels of abstractions in the analysis of interactions, based on both the hierarchical organization of clinical guidelines (in which composite actions are refined into their components) and the hierarchy of drug categories. We then propose a general methodology (data/knowledge structures and reasoning algorithms operating on them) supporting user-driven and flexible interaction detection over the multiple levels of abstraction. Finally, we discuss the impact of the adoption of computerized clinical guidelines in general, and of our methodology in particular, for patients (quality of the received healthcare services), physicians (decision support and quality of provided services), and healthcare managers and organizations (quality and optimization of provided services).

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Section: Discussionmentioning

confidence: 90%

“…This sets up the urgent need of developing ways of merging multiple single-disease interventions to provide professionals' assistance to comorbid patients". (Riaño and Collado 2013).…”

Section: A Critical Issue: the Treatment Of Comorbid Patientsmentioning

confidence: 99%

Section: A Critical Issue: the Treatment Of Comorbid Patientsmentioning

confidence: 99%

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An ontological knowledge and multiple abstraction level decision support system in healthcare

2014

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“…A model-based automatic merge of CIGs is then proposed in [20] through the definition of a combining operator.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Temporal reasoning techniques for the analysis of interactions in the treatment of comorbid patients

Anselma

Piovesan

Terenziani

2017

Proceedings of the Symposium on Applied Computing

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“…The treatment of patient affected by multiple diseases (comorbid patients) is one of the main challenges for the modern healthcare, also due to the aging of population, and the consequent increase of chronic diseases. This sets up the urgent need of developing ways of merging multiple single-disease interventions to provide professionals' assistance to comorbid patients (Riaño and Collado 2013).…”

Section: Introductionmentioning

confidence: 99%

Supporting Multi-level User-driven Detection of Guideline Interactions

Piovesan

Molino

Terenziani

2015

Proceedings of the International Conference on Health Informatics

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Clinical practice guidelines are widely used to support physicians, but only on individual pathologies. The treatment of patients affected by multiple diseases (comorbid patients) requires the development of new approaches, supporting physicians in the detection of interactions between guidelines. We propose a new methodology, supporting flexible and physician-driven search and detection. In particular, we provide a flexible and interactive mechanism to navigate guidelines and our ontology of interactions (between drugs, or between actions' goals) at multiple levels of detail, focusing on specific parts of it (e.g., on a specific pair of actions, or of drugs) to look for interactions. We introduce the notion of "navigation tree", as the basic data structure to support multiple-level interaction analysis, and describe navigation and focusing algorithms operating on it. We also introduce a visualization tool that is based on the "navigation tree", and further enhances the user-friendliness of our approach.

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Model-Based Combination of Treatments for the Management of Chronic Comorbid Patients

Cited by 25 publications

References 7 publications

An ontological knowledge and multiple abstraction level decision support system in healthcare

An ontological knowledge and multiple abstraction level decision support system in healthcare

Temporal reasoning techniques for the analysis of interactions in the treatment of comorbid patients

Supporting Multi-level User-driven Detection of Guideline Interactions

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