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Large scale veterinary clinical records can become a powerful resource forpatient care and research. However, clinicians lack the time and resource toannotate patient records with standard medical diagnostic codes and mostveterinary visits are captured in free text notes. The lack of standard codingmakes it challenging to use the clinical data to improve patient care. It isalso a major impediment to cross-species translational research, which relieson the ability to accurately identify patient cohorts with specific diagnosticcriteria in humans and animals. In order to reduce the coding burden forveterinary clinical practice and aid translational research, we have developeda deep learning algorithm, DeepTag, which automatically infers diagnostic codesfrom veterinary free text notes. DeepTag is trained on a newly curated datasetof 112,558 veterinary notes manually annotated by experts. DeepTag extendsmulti-task LSTM with an improved hierarchical objective that captures thesemantic structures between diseases. To foster human-machine collaboration,DeepTag also learns to abstain in examples when it is uncertain and defers themto human experts, resulting in improved performance. DeepTag accurately infersdisease codes from free text even in challenging cross-hospital settings wherethe text comes from different clinical settings than the ones used fortraining. It enables automated disease annotation across a broad range ofclinical diagnoses with minimal pre-processing. The technical framework in thiswork can be applied in other medical domains that currently lack medical codingresources.

DeepTag: inferring all-cause diagnoses from clinical notes in under-resourced medical domain