Computer technologies to integrate medical treatments to manage multimorbidity

Riaño, David; Ortega, Wilfrido

doi:10.1016/j.jbi.2017.09.009

Cited by 26 publications

(16 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Non-argumentative approaches to reasoning with clinical guidelines exist too, see [75,82] for overviews. A recent work concerning reasoning with interacting guidelines, patient conditions and preferences represents guideline recommendations as actionable graphs [105], mapping them into first-order logic (FOL) rules, while representing patient conditions and preferences as FOL revision operators.…”

Section: Non-argumentative Approaches To Medical Decision Makingmentioning

confidence: 99%

“…Easing the application of clinical guidelines is the objective of the Transition-based Medical Recommendation model (TMR) [108,109], a state-of-the-art formalism [82] for representing computerised clinical guideline recommendations. TMR components and relations reflect knowledge and occurrences typical of multimorbidity situations: the basic components are clinical care actions and their respective effects on the patient's physical properties; the relations amount to interactions among those actions and their effects.…”

Section: Introductionmentioning

confidence: 99%

“…Reasoning is also limited in several other proposed formalisms for clinical guideline representation, particularly when conflicts come into play [41,75,82]. (A notable exception is the recent CONSULT project [19,20,59,107], which we discuss in Section 7.2.)…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Čyras

Oliveira

Karamlou

et al. 2021

AAC

View full text Add to dashboard Cite

A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realistic personalised clinical reasoning scenaria.

show abstract

Section: Non-argumentative Approaches To Medical Decision Makingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Čyras

Oliveira

Karamlou

et al. 2021

AAC

View full text Add to dashboard Cite

show abstract

“…13. A CDSS has to provide intelligent methods for integrating data and information about single-disease treatments to handle comorbidities and multi-morbidities that normally exist in patients with diabetes [150] 14. A CDSS has to provide comprehensive treatments, including alerts, recommendations, SMS messages, and plans regarding the patient's medicines, drug interactions, lifestyle (diet and exercise), and education.…”

Section: B Mh and Cdss Challenges And Future Directionsmentioning

confidence: 99%

Mobile Health Technologies for Diabetes Mellitus: Current State and Future Challenges

et al. 2019

View full text Add to dashboard Cite

The prevalence of diabetes is rising globally. Diabetes patients need continuous monitoring, and to achieve this objective, they have to be engaged in their healthcare management process. Mobile health (MH) is an information and communications technology trend to empower chronically ill patients in a smart environment. Discussing the current state of MH technologies is required in order to address their limitations. Existing review articles have evaluated the MH literature based on applicability and level of adoption by patients and healthcare providers. Most of these reviews asserted that MH apps and research have not reached a stable level yet. To the best of our knowledge, there is no clear description of solutions to these problems. In addition, no one has investigated and analyzed MH in its contextual environment in a detailed way. We conducted a comprehensive survey of MH research on diabetes management articles published between 2011 and September 27, 2017. In this survey, we discuss current challenges in MH, along with research gaps, opportunities, and trends. Our literature review searched three academic databases (ScienceDirect, IEEE Xplore, and SpringerLink). A total of 60 articles were analyzed, with 30% from ScienceDirect, 38% from IEEE Xplore, and 32% from SpringerLink. MH was analyzed in the context of the electronic health record (EHR) ecosystem. We consider dimensions such as clinical decision support systems, EHRs, cloud computing, semantic interoperability, wireless body area networks, and big data analytics. We propose specific metrics to analyze and evaluate MH from each of these dimensions. A comprehensive analysis of the literature from this viewpoint is valuable for both theoretical and developmental progress. This paper provides a critical analysis of challenges that have not been fully met and highlights directions for future research that could improve MH applicability. INDEX TERMS Mobile health, diabetes mellitus, electronic health record, cloud computing, clinical decision support system, big data analytics, wireless body area network, medical informatics.

show abstract

“…Many efforts have been made to generate guidelines for the most common possible multimorbidity conditions, and to report and broadcast the available evidence-based medical knowledge. In the last decades, part of the medical informatics community has, therefore, been working extensively to develop computing structures that could be exploited to convey such knowledge, to support the educational needs of clinicians and medical students [12][13][14]. In particular, Petri Nets have widely been used in the medical domain for workflows modelling [15,16], access to services management [17], resource management [18], or guidelines [19], to figure out the flow of planned and executed activities to accomplish specific tasks, or to solve clinical problems.…”

Section: Introductionmentioning

confidence: 99%

The Friendly Health Issue Network to Support Computer-Assisted Education for Clinical Reasoning in Multimorbidity Patients

et al. 2021

View full text Add to dashboard Cite

Clinical reasoning in multimorbidity conditions asks for the ability to anticipate the possible evolutions of the overall health state of a patient and to identify the interactions among the concurrent health issues and their treatments. The HIN (Health Issue Network) approach, as Petri Nets-based formal language, is introduced as capable of providing a novel perspective to facilitate the acquisition of such competencies, graphically representing the network among a set of health issues (HIs) that affect a person throughout their life, and describing how HIs evolve over time. The need to provide a more immediate user-oriented interface has led to the development of f-HIN (friendly HIN), a lighter version based on the same mathematical properties as HIN, from which stems in turn the f-HINe (friendly HIN extracted) model, used to represent networks related to either real patients’ clinical experiences extracted from electronic health records, or from teacher-designed realistic clinical histories. Such models have also been designed to be embedded in a software learning environment that allows drawing a f-HIN diagram, checking for its format correctness, as well as designing clinical exercises for the learners, including their computer-assisted assessment. The present paper aims at introducing and discussing the f-HIN/f-HINe models and their educational use. It also introduces the main features of the software learning environment it was built upon, pointing out its importance to: (i) help medical teachers in designing and representing the context of a learning outcome; and (ii) handle the complex history of a multimorbidity patient, to be conveyed in Case-Based Learning (CBL) exercises.

show abstract

Computer technologies to integrate medical treatments to manage multimorbidity

Cited by 26 publications

References 58 publications

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Mobile Health Technologies for Diabetes Mellitus: Current State and Future Challenges

The Friendly Health Issue Network to Support Computer-Assisted Education for Clinical Reasoning in Multimorbidity Patients

Contact Info

Product

Resources

About