Abstract
Temporal phenotyping enables clinicians to better under-stand observable characteristics of a disease as it progresses. Modelling disease progression that captures interactions between phenotypes is inherently challenging. Temporal models that capture change in disease over time can identify the key features that characterize disease subtypes that underpin these trajectories. These models will enable clinicians to identify early warning signs of progression in specific sub-types and therefore to make informed decisions tailored to individual patients. In this paper, we explore two approaches to building temporal phenotypes based on the topology of data: topological data analysis and pseudo time-series. Using type 2 diabetes data, we show that the topological data analysis approach is able to identify trajectories representing different temporal phenotypes and that pseudo time-series can infer a state space model characterized by transitions between hidden states that represent distinct temporal phenotypes. Both approaches highlight lipid profiles as key factors in distinguishing the phenotypes.
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Acknowledgement
This work was co-funded by the Medical Research Council and the Engineering and Physical Sciences Research Council grant MR/N00583X/1 “Manchester Molecular Pathology Innovation Centre (MMPathIC): bridging the gap between biomarker discovery and health and wealth” and the NIHR Manchester Biomedical Research Centre.
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Dagliati, A. et al. (2019). Inferring Temporal Phenotypes with Topological Data Analysis and Pseudo Time-Series. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_50
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DOI: https://doi.org/10.1007/978-3-030-21642-9_50
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