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Title

Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network
Authors
Kalweit, Maria
Walker, Ulrich A.
Finckh, Axel
Müller, Rüdiger
Kalweit, Gabriel
Scherer, Almut
Boedecker, Joschka
Hügle, Thomas
Published in PloS one. 2021, vol. 16, no. 6, e0252289
Abstract Background: Deep neural networks learn from former experiences on a large scale and can be used to predict future disease activity as potential clinical decision support. AdaptiveNet is a novel adaptive recurrent neural network optimized to deal with heterogeneous and missing clinical data. Objective: We investigate AdaptiveNet for the prediction of individual disease activity in patients from a rheumatoid arthritis (RA) registry. Methods: Demographic and disease characteristics from over 9500 patients and 65.000 visits from the Swiss Quality Management (SCQM) database were used to train and evaluate the network. Patient characteristics, clinical and patient reported outcomes, laboratory values and medication were used as input features. DAS28-BSR served as a target to predict active RA and future numeric individual disease activity by classification and regression.
Keywords Antirheumatic Agents / therapeutic use — Arthritis — Rheumatoid / drug therapy — Arthritis — Rheumatoid / pathology* — Female — Humans — Linear Models — Male — Middle Aged — Neural Networks — Computer — Prospective Studies — Registries — Sensitivity and Specificity — Severity of Illness Index — Support Vector Machine
Identifiers
PMID: 34185794
Full text
Article (Published version) (1.2 MB) - public document Free access
Other version: https://dx.plos.org/10.1371/journal.pone.0252289
Structures
Faculté de médecine / Section de médecine clinique / Département de médecine
Research group Recherche clinique en rhumatismes inflammatoires (1010)
Citation
(ISO format) 		KALWEIT, Maria et al. Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network. In: PLOS ONE, 2021, vol. 16, n° 6, p. e0252289. doi: 10.1371/journal.pone.0252289 https://archive-ouverte.unige.ch/unige:157626

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Deposited on : 2021-12-23

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