Improving fine-tuned question answering models for electronic health records
Tittaya Mairittha, Nattaya Mairittha, Sozo Inoue · 2020
The prevalence of voice assistants has strengthened the interest in a question answering for the medical domain, allowing both patients and healthcare providers to enter a question naturally and pinpoint useful information quickly. However, a large number of medical terms make the creation of such a system a demanding task. To address this challenge, we explore transfer learning techniques for constructing a personalized EHR-QA system. The goal is to answer questions regarding a discharge summary in an electronic health record (EHR). We present the experiments with a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model fine-tuned on different tasks and show the results obtained to provide insights into learning effects and training effectiveness.