Improving Fine-tuned Question Answering Models for Electronic Health Records
Reviewed, Featured
Tittaya Mairittha, Nattaya Mairittha, Sozo Inoue,
Ubicomp Workshop on Computing for Well-Being (WellComp)
(Not Available)
(Not Available)
688 - 691
2020-09-10
Mexico (virtual)
https://doi.org/10.1145/3410530.3414436
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 makes 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.
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 makes 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.