A Dynamic Deep Neural Network For Multimodal Clinical Data Analysis
This work addresses the challenge of applying deep learning to multimodal clinical data for precision medicine, offering a domain-specific solution for healthcare researchers and practitioners.
The paper tackled the problem of disease progression prediction in rheumatoid arthritis using clinical data, proposing AdaptiveNet, a recurrent neural network architecture that handles missing values and variable-sized lists, and it outperformed classical baselines on a dataset of over 10,000 patients and 65,000 visits.
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.