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MitPlan 2.0: Enhanced Support for Multi-morbid Patient Management Using Planning

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Artificial Intelligence in Medicine (AIME 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12721))

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Abstract

The complexity of patient care is growing due to an ageing population. As chronic illnesses become more common, the incidence of multi-morbidity increases. Generating disease management plans for multi-morbid patients requires the integration of multiple evidence-based interventions, represented as clinical practice guidelines (CPGs), that are designed to treat a single condition. Our previous work developed a mitigation framework called MitPlan that represented the generation of treatment as a planning problem. The framework used the Planning Domain Definition Language (PDDL) to represent clinical and patient information needed to identify and mitigate adverse interactions resulting from the concurrent application of multiple CPGs for a given patient encounter. In this paper we describe MitPlan 2.0 that supports shared decision-making by identifying a treatment plan optimized according to patient preferences, treatment cost, or perceived patient’s adherence to medication. It mitigates adverse interactions using planning constructs, eliminating the need for procedural handling of adverse interactions and as such provides flexible and comprehensive decision support at the point of care. We demonstrate MitPlan 2.0’s extended capabilities using synthetic scenarios approximating real-world clinical use cases and demonstrate its new capabilities within the context of atrial fibrillation.

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Acknowledgements

We thank Andrew Coles and Amanda Coles for their clarifications regarding PDDL and OPTIC and the reviewers for their helpful comments.

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Correspondence to Martin Michalowski .

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Michalowski, M., Rao, M., Wilk, S., Michalowski, W., Carrier, M. (2021). MitPlan 2.0: Enhanced Support for Multi-morbid Patient Management Using Planning. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_31

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  • DOI: https://doi.org/10.1007/978-3-030-77211-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77210-9

  • Online ISBN: 978-3-030-77211-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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