A General Fine-tuned 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 (MIMIC-III and eICU) and one clinical task (in-hospital 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 pre-training model, transferred it to the target dataset and fine-tuned the whole network on target dataset. Finally, we use the fine-tuned 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.