36 research outputs found
Neural Question Answering at BioASQ 5B
This paper describes our submission to the 2017 BioASQ challenge. We
participated in Task B, Phase B which is concerned with biomedical question
answering (QA). We focus on factoid and list question, using an extractive QA
model, that is, we restrict our system to output substrings of the provided
text snippets. At the core of our system, we use FastQA, a state-of-the-art
neural QA system. We extended it with biomedical word embeddings and changed
its answer layer to be able to answer list questions in addition to factoid
questions. We pre-trained the model on a large-scale open-domain QA dataset,
SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our
approach, we achieve state-of-the-art results on factoid questions and
competitive results on list questions
How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering
Using deep learning models on small scale datasets would result in
overfitting. To overcome this problem, the process of pre-training a model and
fine-tuning it to the small scale dataset has been used extensively in domains
such as image processing. Similarly for question answering, pre-training and
fine-tuning can be done in several ways. Commonly reading comprehension models
are used for pre-training, but we show that other types of pre-training can
work better. We compare two pre-training models based on reading comprehension
and open domain question answering models and determine the performance when
fine-tuned and tested over BIOASQ question answering dataset. We find open
domain question answering model to be a better fit for this task rather than
reading comprehension model
Question answering systems for health professionals at the point of care -- a systematic review
Objective: Question answering (QA) systems have the potential to improve the
quality of clinical care by providing health professionals with the latest and
most relevant evidence. However, QA systems have not been widely adopted. This
systematic review aims to characterize current medical QA systems, assess their
suitability for healthcare, and identify areas of improvement.
Materials and methods: We searched PubMed, IEEE Xplore, ACM Digital Library,
ACL Anthology and forward and backward citations on 7th February 2023. We
included peer-reviewed journal and conference papers describing the design and
evaluation of biomedical QA systems. Two reviewers screened titles, abstracts,
and full-text articles. We conducted a narrative synthesis and risk of bias
assessment for each study. We assessed the utility of biomedical QA systems.
Results: We included 79 studies and identified themes, including question
realism, answer reliability, answer utility, clinical specialism, systems,
usability, and evaluation methods. Clinicians' questions used to train and
evaluate QA systems were restricted to certain sources, types and complexity
levels. No system communicated confidence levels in the answers or sources.
Many studies suffered from high risks of bias and applicability concerns. Only
8 studies completely satisfied any criterion for clinical utility, and only 7
reported user evaluations. Most systems were built with limited input from
clinicians.
Discussion: While machine learning methods have led to increased accuracy,
most studies imperfectly reflected real-world healthcare information needs. Key
research priorities include developing more realistic healthcare QA datasets
and considering the reliability of answer sources, rather than merely focusing
on accuracy.Comment: Accepted to the Journal of the American Medical Informatics
Association (JAMIA