Abstract
Diagnosis prediction plays a key role in clinical
decision supporting process, which attracted extensive research attention recently. Existing studies mainly utilize discrete medical codes (e.g., the
ICD codes and procedure codes) as the primary features in prediction. However, in real clinical settings, such medical codes could be either incomplete or erroneous. For example, missed diagnosis will neglect some codes which should be included, mis-diagnosis will generate incorrect medical codes. To increase the robustness towards noisy
data, we introduce textual clinical notes in addition
to medical codes. Combining information from
both sides will lead to improved understanding towards clinical health conditions. To accommodate
both the textual notes and discrete medical codes
in the same framework, we propose Multimodal
Attentional Neural Networks (MNN), which integrates multi-modal data in a collaborative manner.
Experimental results on real world EHR datasets
demonstrate the advantages of MNN in term of accuracy