Longitudinal Healthcare Analytics for Disease Management: Empirical Demonstration for Low Back Pain
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Author / Producer
Date
2020-05
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
Clinician guidelines recommend health management to tailor the form of care to the expected course of diseases. Hence, in order to decide upon a suitable treatment plan, health professionals benefit from decision support, i.e., predictions about how a disease is to evolve. In clinical practice, such a prediction model requires interpretability. Interpretability, however, is often precluded by complex dynamic models that would be capable of capturing the intrapersonal variability of disease trajectories. Therefore, we develop a cross-sectional ARMA model that allows for inference of the expected course of symptoms. Distinct from traditional time series models, it generalizes to cross-sectional settings and thus patient cohorts (i.e., it is estimated to multiple instead of single disease trajectories). Our model is evaluated according to a longitudinal 52-week study involving 928 patients with low back pain. It achieves a favorable prediction performance while maintaining interpretability. In sum, we provide decision support by informing health professionals about whether symptoms will have the tendency to stabilize or continue to be severe.
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Publication status
published
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Book title
Journal / series
Volume
132
Pages / Article No.
113271
Publisher
Elsevier
Event
Edition / version
Methods
Software
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Date collected
Date created
Subject
Healthcare analytics; Disease management; Longitudinal monitoring; Time series analysis; Cohort data; Low back pain
Organisational unit
09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
Notes
Funding
186932 - Data-driven health management (SNF)