Large-scale Generative Modeling to Improve Automated Veterinary Disease Coding
This work addresses the challenge of domain shift and data scarcity in veterinary medical record coding, which is incremental as it applies generative modeling to a known bottleneck in a specific domain.
The authors tackled the problem of automated disease coding in veterinary medicine, where domain shift between hospitals and lack of labeled data limit supervised learning, by proposing the first large-scale generative modeling algorithm; they demonstrated that it outperforms competitive baselines by a large margin in cross-hospital settings.
Supervised learning is limited both by the quantity and quality of the labeled data. In the field of medical record tagging, writing styles between hospitals vary drastically. The knowledge learned from one hospital might not transfer well to another. This problem is amplified in veterinary medicine domain because veterinary clinics rarely apply medical codes to their records. We proposed and trained the first large-scale generative modeling algorithm in automated disease coding. We demonstrate that generative modeling can learn discriminative features when additionally trained with supervised fine-tuning. We systematically ablate and evaluate the effect of generative modeling on the final system's performance. We compare the performance of our model with several baselines in a challenging cross-hospital setting with substantial domain shift. We outperform competitive baselines by a large margin. In addition, we provide interpretation for what is learned by our model.