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Scientific article
Open access
English

Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network

Published inPLOS ONE, vol. 16, no. 6, e0252289
Publication date2021
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
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
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Article (Published version)
Identifiers
ISSN of the journal1932-6203
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Technical informations

Creation08/30/2021 4:19:00 PM
First validation08/30/2021 4:19:00 PM
Update time03/16/2023 2:15:14 AM
Status update03/16/2023 2:15:13 AM
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