A Microservice Architecture for the Design of Computer-Interpretable Guideline Processing Tools

Chapman, Martin; Ćurčin, Vasa

doi:10.1109/eurocon.2019.8861830

Cited by 6 publications

(12 citation statements)

References 13 publications

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“…As a specialised type of service-oriented architecture, the microservice approach dictates that each service should only deliver one specific piece of system functionality, making it easier to achieve quality attributes such as scalability and resilience in practice 13 . This paradigm has been successfully used to structure software in several health domains, including the representation of clinical guidelines 14 .…”

Section: Methodsmentioning

confidence: 99%

“…Software designed as a microservice architecture provide functionality based on the interactions between individual services, each of which can be replicated in order to improve attributes such as scalability and resilience 11 . This architecture paradigm has been successfully used to structure software in a number of health domains, including the representation of clinical guidelines 12 . Providing an implementation unit for the third step in the abstract and functional layers, in order to generate a step in the computational layer and store this unit.…”

Section: Generating Computable Phenotypesmentioning

confidence: 99%

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Phenoflow: A Microservice Architecture for Portable Workflow-based Phenotype Definitions

Chapman

Rasmussen

Pacheco

et al. 2020

Preprint

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Clinical phenotyping is an effective way to identify patients with particular characteristics within a population. In order to enhance the portability of a phenotype, it is often defined abstractly, with users expected to realise the phenotype computationally before executing it against a local dataset. However, complex definitions, which also provide little information about how best to implement a phenotype in practice, mean that this process is often not easy. To address this issue, we propose a new multi-layer model for a phenotype definition, which is realised as a workflow, and can be combined with different implementation units in order to produce a computable form. A novel authoring architecture, Phenoflow, supports the generation of these structured definitions. To illustrate the utility of our approach, we re-author a diabetes phenotype definition, and then compare its portability to the original definition, in the context of a population of 26,406 patients at Northwestern University.

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Section: Methodsmentioning

confidence: 99%

Section: Generating Computable Phenotypesmentioning

confidence: 99%

Phenoflow: A Microservice Architecture for Portable Workflow-based Phenotype Definitions

Chapman

Rasmussen

Pacheco

et al. 2020

Preprint

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“…In terms of reasoning with multiple guidelines in the setting of multimorbidities, the recent CONSULT project [19,20,59,107] applies argumentation to reason with guidelines and patient preferences for managing patients with comorbidities. Specifically, they use structured metalevel argumentation frameworks (MAFs) based on either, essentially, second-order logic as in [107], or first-order logic as in [59], to construct arguments using argumentation schemes and particularly the critical questions pertaining to the latter.…”

Section: Argumentation For Reasoning With Clinical Guidelinesmentioning

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.) Additionally, the representations afforded by such formalisms rarely take into account the context of the patient, namely patient-specific conditions, patient-centric goals, and preferences from the various parties involved [75,83,99].…”

Section: Introductionmentioning

confidence: 99%

“…Very importantly, ABA + is equipped with working implementations, for instance the stand-alone 1 and web 2 applications as described in [7] and a stand-alone development 3 built on [56]. These make it easy to implement ABA + G, connect it to TMR (via an implementation 4 of [109] and its programming interface TMRweb [20]) and thus lay grounds for the decision support system in question.…”

Section: Introductionmentioning

confidence: 99%

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

Čyras

Oliveira

Karamlou

et al. 2021

AAC

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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.

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ROAD2H: Development and evaluation of an open‐source explainable artificial intelligence approach for managing co‐morbidity and clinical guidelines

Domínguez,

Prociuk,

Marović

et al. 2023

Learning Health Systems

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IntroductionClinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real‐world co‐morbidity. In patient‐specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co‐morbidity are a requirement. In this work, we develop and evaluate a non‐proprietary, standards‐based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle‐income country in West Balkans.MethodsWe used an ontological framework, the Transition‐based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR‐based implementation of the Assumptions‐Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed‐methods evaluation was conducted with 20 simulated cases and five pulmonologists.ResultsPulmonologists agreed 97% of the time with the GOLD‐based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co‐morbidities was suggested in the future along with customisation of the level of explanation with expertise.ConclusionAn ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long‐term condition. Extension to other guidelines and multiple co‐morbidities is needed to test the approach further.

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A Microservice Architecture for the Design of Computer-Interpretable Guideline Processing Tools

Cited by 6 publications

References 13 publications

Phenoflow: A Microservice Architecture for Portable Workflow-based Phenotype Definitions

Phenoflow: A Microservice Architecture for Portable Workflow-based Phenotype Definitions

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

ROAD2H: Development and evaluation of an open‐source explainable artificial intelligence approach for managing co‐morbidity and clinical guidelines

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