Developing the Interpretability of Deep Artificial Neural Network on Application Problems In recent years, the use of electronic health records (EHR) has increased dramatically. Mining hidden knowledge in xe2x80x9cbig dataxe2x80x9d from EHR has become a subject worthy of exploration. On the other hand, many recent applications used deep artificial neural network (ANN) to analyze EHR data and yielded great performance. Accordingly, this study developed functional models using deep ANN, and tried to validate effectiveness of this method in regression analysis and classification problem. Based on datasets downloaded from the UC Irvine Machine Learning Repository, the output mean squared error value 0.840 was within the range of one variance for the regression analysis. Similarly, the prediction accuracy 73.0% on the testing data was reported for the classification problem. Another focus of this study was identifying critical attributes using the layerwise relevance propagation (LRP) algorithm to improve interpretability of deep ANN. According to evaluation outcomes, the identified features would match with those recognized by univariate analysis. In summary, effectiveness of deep ANN and LRP on application problems has been validated in this study.

Deep learning for electronic health records: A comparative review of multiple deep neural architectures Abstract   Despite the recent developments in deep learning models, their applications in clinical decisionsupport systems have been very limited. Recent digitalisation of health records, however, has provided a great platform for the assessment of the usability of such techniques in healthcare. As a result, the field is starting to see a growing number of research papers that employ deep learning on electronic health records (EHR) for personalised prediction of risks and health trajectories. While this can be a promising trend, vast papertopaper variability (from data sources and models they use to the clinical questions they attempt to answer) have hampered the fieldxe2x80x99s ability to simply compare and contrast such models for a given application of interest. Thus, in this paper, we aim to provide a comparative review of the key deep learning architectures that have been applied to EHR data. Furthermore, we also aim to: (1) introduce and use one of the worldxe2x80x99s largest and most complex linked primary care EHR datasets (i.e., Clinical Practice Research Datalink, or CPRD) as a new asset for training such datahungry models; (2) provide a guideline for working with EHR data for deep learning; (3) share some of the best practices for assessing the xe2x80x9cgoodnessxe2x80x9d of deeplearning models in clinical risk prediction; (4) and propose future research ideas for making deep learning models more suitable for the EHR data. Our results highlight the difficulties of working with highly imbalanced datasets, and show that sequential deep learning architectures such as RNN may be more suitable to deal with the temporal nature of EHR.