4,205 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
Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents
Recent advances in the healthcare industry have led to an abundance of
unstructured data, making it challenging to perform tasks such as efficient and
accurate information retrieval at scale. Our work offers an all-in-one scalable
solution for extracting and exploring complex information from large-scale
research documents, which would otherwise be tedious. First, we briefly explain
our knowledge synthesis process to extract helpful information from
unstructured text data of research documents. Then, on top of the knowledge
extracted from the documents, we perform complex information retrieval using
three major components- Paragraph Retrieval, Triplet Retrieval from Knowledge
Graphs, and Complex Question Answering (QA). These components combine lexical
and semantic-based methods to retrieve paragraphs and triplets and perform
faceted refinement for filtering these search results. The complexity of
biomedical queries and documents necessitates using a QA system capable of
handling queries more complex than factoid queries, which we evaluate
qualitatively on the COVID-19 Open Research Dataset (CORD-19) to demonstrate
the effectiveness and value-add
A Survey on Conversational Search and Applications in Biomedicine
This paper aims to provide a radical rundown on Conversation Search
(ConvSearch), an approach to enhance the information retrieval method where
users engage in a dialogue for the information-seeking tasks. In this survey,
we predominantly focused on the human interactive characteristics of the
ConvSearch systems, highlighting the operations of the action modules, likely
the Retrieval system, Question-Answering, and Recommender system. We labeled
various ConvSearch research problems in knowledge bases, natural language
processing, and dialogue management systems along with the action modules. We
further categorized the framework to ConvSearch and the application is directed
toward biomedical and healthcare fields for the utilization of clinical social
technology. Finally, we conclude by talking through the challenges and issues
of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an
integrated and unified vision of the ConvSearch components from different
fields, which benefit the information-seeking process in healthcare systems
Overview of BioASQ 2023: The eleventh BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
This is an overview of the eleventh edition of the BioASQ challenge in the
context of the Conference and Labs of the Evaluation Forum (CLEF) 2023. BioASQ
is a series of international challenges promoting advances in large-scale
biomedical semantic indexing and question answering. This year, BioASQ
consisted of new editions of the two established tasks b and Synergy, and a new
task (MedProcNER) on semantic annotation of clinical content in Spanish with
medical procedures, which have a critical role in medical practice. In this
edition of BioASQ, 28 competing teams submitted the results of more than 150
distinct systems in total for the three different shared tasks of the
challenge. Similarly to previous editions, most of the participating systems
achieved competitive performance, suggesting the continuous advancement of the
state-of-the-art in the field.Comment: 24 pages, 12 tables, 3 figures. CLEF2023. arXiv admin note: text
overlap with arXiv:2210.0685
Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature
Although current state-of-the-art Transformer-based solutions succeeded in a wide range for single-document NLP tasks, they still struggle to address multi-input tasks such as multi-document summarization. Many solutions truncate the inputs, thus ignoring potential summary-relevant contents, which is unacceptable in the medical domain where each information can be vital. Others leverage linear model approximations to apply multi-input concatenation, worsening the results because all information is considered, even if it is conflicting or noisy with respect to a shared background. Despite the importance and social impact of medicine, there are no ad-hoc solutions for multi-document summarization. For this reason, we propose a novel discriminative marginalized probabilistic method (DAMEN) trained to discriminate critical information from a cluster of topic-related medical documents and generate a multi-document summary via token probability marginalization. Results prove we outperform the previous state-of-the-art on a biomedical dataset for multi-document summarization of systematic literature reviews. Moreover, we perform extensive ablation studies to motivate the design choices and prove the importance of each module of our method
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