4 research outputs found
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
BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale
Capturing the semantics of related biological concepts, such as genes and
mutations, is of significant importance to many research tasks in computational
biology such as protein-protein interaction detection, gene-drug association
prediction, and biomedical literature-based discovery. Here, we propose to
leverage state-of-the-art text mining tools and machine learning models to
learn the semantics via vector representations (aka. embeddings) of over
400,000 biological concepts mentioned in the entire PubMed abstracts. Our
learned embeddings, namely BioConceptVec, can capture related concepts based on
their surrounding contextual information in the literature, which is beyond
exact term match or co-occurrence-based methods. BioConceptVec has been
thoroughly evaluated in multiple bioinformatics tasks consisting of over 25
million instances from nine different biological datasets. The evaluation
results demonstrate that BioConceptVec has better performance than existing
methods in all tasks. Finally, BioConceptVec is made freely available to the
research community and general public via
https://github.com/ncbi-nlp/BioConceptVec.Comment: 33 pages, 6 figures, 7 tables, accepted by PLOS Computational Biolog
Multi-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet
Word embedding approaches increased the efficiency of natural language processing (NLP) tasks. Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words. The tasks of semantic similarity between concepts need to understand relations like hypernymy and synonym sets to produce efficient word embeddings. The outcomes of any expert system are affected by the text representation. Systems that understand senses, context, and definitions of concepts while deriving vector representations handle the drawbacks of single vector representations. This paper presents a novel idea for handling polysemy by generating Multi-Sense Embeddings using synonym sets and hypernyms information of words. This paper derives embeddings of a word by understanding the information of a word at different levels, starting from sense to context and definitions. Proposed sense embeddings of words obtained prominent results when tested on word similarity tasks. The proposed approach is tested on nine benchmark datasets, which outperformed several state-of-the-art systems