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dc.contributor.authorDagliati, A-
dc.contributor.authorGeifman, N-
dc.contributor.authorPeek, N-
dc.contributor.authorHolmes, JH-
dc.contributor.authorSacchi, L-
dc.contributor.authorBellazzi, R-
dc.contributor.authorSajjadi, SE-
dc.contributor.authorTucker, A-
dc.identifier.citationDagliati A, Geifman N, Peek N, Holmes JH, Sacchi L, Bellazzi R, Sajjadi SE, Tucker A. Using Topological Data Analysis and Pseudo Time Series to Infer Temporal Phenotypes from Electronic Health Records. Artificial Intelligence in Medicine. 2020 Jul 15:101930.en_US
dc.description.abstractTemporal phenotyping enables clinicians to better understand 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 disease trajectories 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.en_US
dc.description.sponsorshipMedical Research Council; Engineering and Physical Sciences Research Council; National Institute for Health Researchen_US
dc.format.extent1 - 14 (14)-
dc.subjectType 2 diabetesen_US
dc.subjectUnsupervised machine learningen_US
dc.subjectLongitudinal studiesen_US
dc.subjectElectronic phenotypingen_US
dc.titleUsing topological data analysis and pseudo time series to infer temporal phenotypes from electronic health recordsen_US
dc.relation.isPartOfArtificial Intelligence in Medicine-
pubs.issueAugust 2020-
Appears in Collections:Dept of Computer Science Research Papers

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