MuCIGREF: multiple computer-interpretable guideline representation and execution framework for managing multimobidity care

Clinical Practice Guidelines (CPGs) supply evidence-based recommendations to healthcare professionals (HCPs) for the care of patients. Their use in clinical practice has many benefits for patients, HCPs and treating medical centres, such as enhancing the quality of care, and reducing unwanted care variations. However, there are many challenges limiting their implementations. Initially, CPGs predominantly consider a specific disease, and only few of them refer to multimorbidity (i.e. the presence of two or more health conditions in an individual) and they are not able to adapt to dynamic changes in patient health conditions. The manual management of guideline recommendations are also challenging since recommendations may adversely interact with each other due to their competing targets and/or they can be duplicated when multiple of them are concurrently applied to a multimorbid patient. These may result in undesired outcomes such as severe disability, increased hospitalisation costs and many others. Formalisation of CPGs into a Computer Interpretable Guideline (CIG) format, allows the guidelines to be interpreted and processed by computer applications, such as Clinical Decision Support Systems (CDSS). This enables provision of automated support to manage the limitations of guidelines. This thesis introduces a new approach for the problem of combining multiple concurrently implemented CIGs and their interrelations to manage multimorbidity care. MuCIGREF (Multiple Computer-Interpretable Guideline Representation and Execution Framework), is proposed whose specific objectives are to present (1) a novel multiple CIG representation language, MuCRL, where a generic ontology is developed to represent knowledge elements of CPGs and their interrelations, and to create the multimorbidity related associations between them. A systematic literature review is conducted to discover CPG representation requirements and gaps in multimorbidity care management. The ontology is built based on the synthesis of well-known ontology building lifecycle methodologies. Afterwards, the ontology is transformed to a metamodel to support the CIG execution phase; and (2) a novel real-time multiple CIG execution engine, MuCEE, where CIG models are dynamically combined to generate consistent and personalised care plans for multimorbid patients. MuCEE involves three modules as (i) CIG acquisition module, transfers CIGs to the personal care plan based on the patient’s health conditions and to supply CIG version control; (ii) parallel CIG execution module, combines concurrently implemented multiple CIGs by performing concurrency management, time-based synchronisation (e.g., multi-activity merging), modification, and timebased optimisation of clinical activities; and (iii) CIG verification module, checks missing information, and inconsistencies to support CIG execution phases. Rulebased execution algorithms are presented for each module. Afterwards, a set of verification and validation analyses are performed involving real-world multimorbidity cases studies and comparative analyses with existing works. The results show that the proposed framework can combine multiple CIGs and dynamically merge, optimise and modify multiple clinical activities of them involving patient data. This framework can be used to support HCPs in a CDSS setting to generate unified and personalised care recommendations for multimorbid patients while merging multiple guideline actions and eliminating care duplications to maintain their safety and supplying optimised health resource management, which may improve operational and cost efficiency in real world-cases, as well

How to Read the Book “Foundations of Biomedical Knowledge Representation”

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How to Read the Book “Foundations of Biomedical Knowledge Representation”

Biology and medicine are very rich knowledge domains in which already at an early stage in their scientific development it was realised that without a proper way to organise this knowledge they would inevitably turn into chaos. Early examples of organisation attempts are for example “De Rerum Natura (On the Nature of Things)” by Titus Lucretius Carus (99–55 BC), which explains the natural and physical world as known at the time, and of course the work “Systema Naturae” by Carl Linnaeus published in 1735. The latter book can be seen as the clear recognition of the need of using systematic methods, here principles of taxonomic organisation, to classify nature. As soon as one considers using systematic methods, computer-based representations and algorithms come to mind

How to Read the Book “Foundations of Biomedical Knowledge Representation”

Biology and medicine are very rich knowledge domains in which already at an early stage in their scientific development it was realised that without a proper way to organise this knowledge they would inevitably turn into chaos. Early examples of organisation attempts are for example “De Rerum Natura (On the Nature of Things)” by Titus Lucretius Carus (99–55 BC), which explains the natural and physical world as known at the time, and of course the work “Systema Naturae” by Carl Linnaeus published in 1735. The latter book can be seen as the clear recognition of the need of using systematic methods, here principles of taxonomic organisation, to classify nature. As soon as one considers using systematic methods, computer-based representations and algorithms come to mind