Temporal Conformance Analysis and Explanation of Clinical Guidelines Execution: An Answer Set Programming Approach

Spiotta, Matteo; Terenziani, Paolo; Dupré, Daniele Theseider

doi:10.1109/tkde.2017.2734084

Cited by 16 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[61,89], or identification of incompatibilities among guidelines. As to the latter, answer set programming is for instance used in [91] to check temporal conformance through a posteriori verification of a single guideline with the recommendations actually followed, motivated by the patient state. On the other hand, statistical preference learning is used in [96] to identify inconsistencies in antibiotic therapy guidelines.…”

Section: Non-argumentative Approaches To Medical Decision Makingmentioning

confidence: 99%

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%

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

“…However, there is no reasoning mechanism to deal with conflicting recommendations. Spiotta et al [21] propose a framework to analyze the temporal conformance of followed actions against a single CIG. Using answer set programming they provide explanations on conflicting situations, based on events in the state of a patient.…”

Section: Related Workmentioning

confidence: 99%

Goal-Driven Structured Argumentation for Patient Management in a Multimorbidity Setting

Oliveira

Dauphin

Satoh

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We use computational argumentation to both analyse and generate solutions for reasoning in multimorbidity about consistent recommendations, according to different patient-centric goals. Reasoning in this setting carries a complexity related to the multiple variables involved. These variables reflect the co-existing health conditions that should be considered when defining a proper therapy. However, current Clinical Decision Support Systems (CDSSs) are not equipped to deal with such a situation. They do not go beyond the straightforward application of the rules that build their knowledge base and simple interpretation of Computer-Interpretable Guidelines (CIGs). We provide a computational argumentation system equipped with goal-seeking mechanisms to combine independently generated recommendations, with the ability to resolve conflicts and generate explanations for its results. We also discuss its advantages over and relation to Multiple-criteria Decisionmaking (MCDM) in this particular setting.

show abstract

“…[20,33], or identification of incompatibilities among guidelines, e.g. answer set programming is used in [34] to check temporal conformance; statistical preference learning is used in [36] to identify inconsistencies in antibiotic therapy guidelines. Yet other works concern preference elicitation to facilitate shared (clinician-patient) decision making.…”

Section: Related Workmentioning

confidence: 99%

Resolving Conflicts in Clinical Guidelines using Argumentation

Čyras,

Oliveira

2019

Preprint

View full text Add to dashboard Cite

Automatically reasoning with conflicting generic clinical guidelines is a burning issue in patient-centric medical reasoning where patient-specific conditions and goals need to be taken into account. It is even more challenging in the presence of preferences such as patient's wishes and clinician's priorities over goals. We advance a structured argumentation formalism for reasoning with conflicting clinical guidelines, patient-specific information and preferences. Our formalism integrates assumption-based reasoning and goal-driven selection among reasoning outcomes. Specifically, we assume applicability of guideline recommendations concerning the generic goal of patient well-being, resolve conflicts among recommendations using patient's conditions and preferences, and then consider prioritised patient-centered goals to yield non-conflicting, goal-maximising and preference-respecting recommendations. We rely on the state-of-the-art Transition-based Medical Recommendation model for representing guideline recommendations and augment it with context given by the patient's conditions, goals, as well as preferences over recommendations and goals. We establish desirable properties of our approach in terms of sensitivity to recommendation conflicts and patient context.

show abstract

Temporal Conformance Analysis and Explanation of Clinical Guidelines Execution: An Answer Set Programming Approach

Cited by 16 publications

References 21 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

Goal-Driven Structured Argumentation for Patient Management in a Multimorbidity Setting

Resolving Conflicts in Clinical Guidelines using Argumentation

Contact Info

Product

Resources

About