Name
Abstract | ||
|---|---|---|
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.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3410530.3414436 | UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers
Virtual Event
Mexico
September, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-8076-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tittaya Mairittha | 1 | 1 | 4.74 |
| Nattaya Mairittha | 2 | 2 | 4.77 |
| Sozo Inoue | 3 | 176 | 58.17 |