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

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.

Results: AdaptiveNet predicted active disease defined as DAS28-BSR >2.6 at the next visit with an overall accuracy of 75.6% (SD +- 0.7%) and a sensitivity and specificity of 84.2% (SD +- 1.6%) and 61.5% (SD +- 3.6%), respectively. Prediction performance was significantly higher in patients with a disease duration >3 years and positive rheumatoid factor. Regression allowed forecasting individual DAS28-BSR values with a mean squared error (MSE) of 0.9 (SD +- 0.05). This corresponds to a 8% deviation between estimated and real DAS28-BSR values. Compared to linear regression, random forest and support vector machines, AdaptiveNet showed an increased performance of over 7% in MSE. Medication played a minor role in the prediction of RA disease activity.

Conclusion: AdaptiveNet has a superior capacity to predict numeric RA disease activity compared to classical machine learning architectures. All investigated models had limitations in low specificity.

Fig 1. Deep neural network architecture (AdaptiveNet).

All visits and medication adjustments are projected to latent vectors of the same size using encoder networks ϕ^visits and ϕ^meds. Latent vectors are sorted according to dates and fed into a Long Short-term Memory (LSTM) to create a latent vector describing the full patient history. The final prediction is computed by the network module ρ, exploiting the patient history with general patient information.

Fig 2. Classification performance of AdaptiveNet to predict active disease (DAS28-BSR>2.6) in different patient subsets shown by Receiver Operating Characteristic Curves.

Accuracy and corresponding AUCs are indicated in Table 1.

Fig 3. Examples of true disease activity and corresponding predictions of AdaptiveNet by regression analysis.

Predictions are made step to step from the current to next visit.