Improving Automated Diagnostic Coding Structures in Veterinary Medicine

DeepTag, an LTSM structure for veterinary medical record text in private and academic hospitals

DeepTag: inferring diagnoses from veterinary clinical notes

Allen Nie, Ashley Zehnder, Rodney L Page, Yuhui Zhang, Arturo Lopez Pineda, Manuel A Rivas, Carlos D Bustamante, James Zou

The study presents the development of DeepTag, an algorithm designed to automatically infer diagnostic codes from veterinary clinical notes, aiming to alleviate the lack of standardized coding infrastructure in veterinary medicine. DeepTag utilizes a bidirectional long-short-term memory network (BLSTM) for training, which extends the multitask LSTM with a hierarchical objective to capture the semantic structures between diseases. It is trained on a dataset of 112,558 manually annotated veterinary notes, enabling automated disease annotation across a broad range of clinical diagnoses with minimal preprocessing. The researchers also address the challenges of cross-hospital coding tasks and the differences in text style between academic and private practice settings.

The paper highlights the scarcity of structured coding and standardized nomenclatures in veterinary medicine, hindering clinical research and public health monitoring efforts. It also emphasizes the potential translational impact of utilizing spontaneous disease models in animals for the study of human diseases, particularly in noninfectious diseases and drug development pipelines. Furthermore, the study emphasizes the need for natural language processing (NLP) tools in the veterinary community to convert free-text clinical notes into structured information, given the abundance of clinical summaries stored as electronic health records in various hospitals and clinics.

Overcoming Challenges

The research team worked on addressing challenges related to the generalization of a tagging system across different academic and private practice datasets, as well as training the system to abstain from making predictions when uncertain. The study proposes novel methods for applying broad disease codes to clinical records and demonstrates the potential implications of such algorithms for real-world implementations, highlighting the significant research required to optimize methods for domain adaptation, as well as the need for expert judgment to improve the overall


DeepTag Algorithm Performance and Future Directions

The DeepTag algorithm was found to outperform baseline models on an external private practice dataset, demonstrating improvements in performance and precision, particularly with learned abstention rules. The study concludes with discussions on future directions, such as leveraging unsupervised representation learning and considering non-disorder disease codes in clinical records for further refinement of automated coding systems in veterinary medicine.

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