A General Finetuned Transfer Learning Model for Predicting Clinical Task Acrossing Diverse EHRs Datasets Data analysis of electronic health record (EHRs) system using machine learning, statistical methods can predict relevant clinical tasks. However, there is no uniform standard for current electronic health record systems, and the clinical outcome prediction models trained on one EHR dataset cannot be applied well on other EHR datasets from different medical institutions. Data differences between different medical institutions pose a huge challenge to the study of electronic health records. In this study, we proposed a general transfer learning strategy which can enable models to make clinical prediction acrossing diverse EHRs datasets and validated its strong versatility on three deep learning models. Two different intensive care units (ICU) databases (MIMICIII and eICU) and one clinical task (inhospital mortality) are used to evaluate our method. At first, we trained the deep learning models on the source dataset and saved the model states after each epoch. Then, we selected the best performing model as the pretraining model, transferred it to the target dataset and finetuned the whole network on target dataset. Finally, we use the finetuned models to make predictions on the target dataset. Experiment results show that AUROC score increased by 3%20% with transfer strategy, which indicated that the general strategy can provide more reliable predictions acrossing EHRs databases to predict clinical tasks.

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.