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Open access
Datum
2021-12Typ
- Review Article
Abstract
In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000525110Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Journal of Medical Internet ResearchBand
Seiten / Artikelnummer
Verlag
JMIR PublicationsThema
patient trajectories; longitudinal data; digital medicine; artificial intelligence; machine learningOrganisationseinheit
09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
Förderung
186932 - Data-driven health management (SNF)