DeepTriager: A Neural Attention Model for Emergency Triage with Electronic Health Records As the first pass for emergency patients, triage is the most important factor affecting emergency department (ED) overcrowding. So it is crucial to develop a datadriven and evidencebased triage method to quickly identify acute and severe patients, and prevent the limited emergency resources from overdiagnosis. To address these challenges, we propose an attention based deep learning framework, named DeepTriager. Trained and tested on 70,918 clinical records, DeepTriager achieved highly accurate performance on assessment of acuity level I (endangered patients), with AUC of 0.98, which was 0.16 higher than the clinical scale method MEWS and NEWS, and 0.04 higher than traditional machine learning methods. In summary, we presented a new approach for clinical evidence based discovery using a cohort of Electronic Health Records (EHRs). This approach not only outperforms the traditional word segmentation methods but also provides evidence for interpreting the results.

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.