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A treatment engine by multimodal EMR data

In recent years, with the development of electronic medical record (EMR) systems, it has become possible to mine patient clinical data to improve medical care quality. After the treatment engine learns knowledge from the EMR data, it can automatically recommend the next stage of prescriptions and provide treatment guidelines for doctors and patients. However, this task is always challenged by the multi-modality of EMR data. To more effectively predict the next stage of treatment prescription by using multimodal information and the connection between the modalities, we propose a cross-modal shared-specific feature complementary generation and attention fusion algorithm. In the feature extraction stage, specific information and shared information are obtained through a shared-specific feature extraction network. To obtain the correlation between the modalities, we propose a sorting network. We use the attention fusion network in the multimodal feature fusion stage to give different multimodal features at different stages with different weights to obtain a more prepared patient representation. Considering the redundant information of specific modal information and shared modal information, we introduce a complementary feature learning strategy, including modality adaptation for shared features, project adversarial learning for specific features, and reconstruction enhancement. The experimental results on the real EMR data set MIMIC-III prove its superiority and each part's effectiveness.