DeepTag: inferring all-cause diagnoses from clinical notes in under-resourced medical domain
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In many under-resourced settings, clinicians lack time and expertise to annotate patients with standard medical diagnosis codes. Veterinary medicine is an example of this and clinical encounters are largely captured in free text notes which are not labeled with diagnosis code. The lack of such standard coding makes it challenging to apply data science to improve patient care. It is also a major impediment to translational research, where, for example, we would like to leverage veterinary data to inform drug development for humans. We develop a deep learning algorithm, DeepTag, to automatically infer diagnosis codes from veterinarian free text notes. DeepTag is trained on a newly curated dataset of 112,558 veterinary notes manually annotated by experts. DeepTag extends multi-task LSTM with an improved hierarchical objective that captures structures between diseases. To foster human-machine collaboration, DeepTag also learns to abstain in examples when it is uncertain and defer them to human experts, resulting in improved performance of the model. DeepTag accurately infers disease codes from free text even in challenging out-of-domain settings where the text comes from different clinics than the ones used for training. It enables automated disease annotation across a broad range of clinical diagnoses with minimal pre-processing. The technical framework in this work can be applied in other medical domains that currently lack medical coding infrastructure.
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