A Dynamic Deep Neural Network For Multimodal Clinical Data Analysis

TL;DR

This paper introduces AdaptiveNet, a novel recurrent neural network architecture designed to handle complex, variable-sized, and incomplete clinical data for improved disease progression prediction in rheumatoid arthritis.

Contribution

The paper presents AdaptiveNet, a new RNN architecture that effectively manages missing data and variable-length sequences in clinical datasets, outperforming traditional methods.

Findings

AdaptiveNet achieves more compact data representations.

It outperforms classical baselines in disease progression prediction.

The approach is validated on a large rheumatoid arthritis registry.

Abstract

Clinical data from electronic medical records, registries or trials provide a large source of information to apply machine learning methods in order to foster precision medicine, e.g. by finding new disease phenotypes or performing individual disease prediction. However, to take full advantage of deep learning methods on clinical data, architectures are necessary that 1) are robust with respect to missing and wrong values, and 2) can deal with highly variable-sized lists and long-term dependencies of individual diagnosis, procedures, measurements and medication prescriptions. In this work, we elaborate limitations of fully-connected neural networks and classical machine learning methods in this context and propose AdaptiveNet, a novel recurrent neural network architecture, which can deal with multiple lists of different events, alleviating the aforementioned limitations. We employ the architecture to the problem of disease progression prediction in rheumatoid arthritis using the Swiss Clinical Quality Management registry, which contains over 10.000 patients and more than 65.000 patient visits. Our proposed approach leads to more compact representations and outperforms the classical baselines.