Abstract
Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning (including deep learning) provides a great opportunity to address this unmet need. In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions in one’s future visits. When trained and evaluated on the data from nearly 1.6 million individuals, BEHRT shows a striking improvement of 8.0–13.2% (in terms of average precision scores for different tasks), over the existing state-of-the-art deep EHR models. In addition to its scalability and superior accuracy, BEHRT enables personalised interpretation of its predictions; its flexible architecture enables it to incorporate multiple heterogeneous concepts (e.g., diagnosis, medication, measurements, and more) to further improve the accuracy of its predictions; its (pre-)training results in disease and patient representations can be useful for future studies (i.e., transfer learning).
Highlights
The field of precision healthcare aims to improve the provision of care through precise and personalised prediction, prevention, and intervention
To further investigate BEHRT’s predictive performance, we carried out three experiments: (1) We investigated if BEHRT can implicitly learn gender and utilise this latent understanding in subsequent visit prediction; (2) we carried out an ablation study by selectively deactivating age, segment, and/or position embeddings and seeing their effects on average precision score (APS) and area under the receiver operating characteristic curve (AUROC); and (3) we assessed the model’s performance on the prediction of new instances of diseases
We introduced a novel deep neural network model for electronic health records (EHR) called BEHRT; an interpretable personalised risk model, which scales across a range of diseases and incorporates a wide range of EHR modalities/ concepts in its modular architecture
Summary
The field of precision healthcare aims to improve the provision of care through precise and personalised prediction, prevention, and intervention. Recent developments in deep learning, provided us with models that can learn useful representations (e.g., of individuals or concepts) from raw or minimally-processed data, with minimal need for expert guidance[9] This happens through a sequence of layers, each employing a large number of simple linear and nonlinear transformations to map their corresponding inputs to a representation; this progress across layers results in a final representation in which the data points form distinguishable patterns. Miotto et al.[12] employed a stack of denoising autoencoders (SDA) instead of RBM, and showed that it outperforms many popular feature extraction and feature transformation approaches (e.g., PCA, ICA13 and Gaussian mixture models) for providing classifiers with useful patient representations to predict the onset of a number of diseases from EHR These early works on the application of DL to EHR did not take into account the subtleties of EHR data (e.g., the irregularity of the inter-visit intervals, and the temporal order or events). Both these works employed some embedding techniques to map non-numeric medical concepts to an algebraic space in which the sequence models can operate
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
