Named Entity Aware Transfer Learning for Biomedical Factoid Question Answering

Peng, Keqin; Yin, Chuantao; Rong, Wenge; Lin, Chenghua; Zhou, Deyu; Zhang, Xiong

doi:10.1109/tcbb.2021.3079339

Cited by 19 publications

(5 citation statements)

References 42 publications

(64 reference statements)

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“…These models were used to find potential answer text spans given questions and passages. Four studies 27 , 35 , 36 , 39 found fine-tuning pretrained LLMs on biomedical data led to improvements in performance compared with only using only a general-domain LLM. No experiments were conducted on LLMs that were trained only on biomedical data.…”

Section: Resultsmentioning

confidence: 99%

Question answering systems for health professionals at the point of care—a systematic review

Kell,

Roberts,

Umansky

et al. 2024

Journal of the American Medical Informatics Association

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Objectives 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 February 7, 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.

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Section: Resultsmentioning

confidence: 99%

Question answering systems for health professionals at the point of care—a systematic review

Kell,

Roberts,

Umansky

et al. 2024

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

show abstract

“…The model also uses bagging to further improve its overall performance, which better combines question-level and token-level information. The model has been evaluated on BioASQ 6b and 7b datasets, and the results demonstrate its advantages and promising potentials [29]. When adapting to specialized domains, such as the COVID-19 literature, model fine-tuning and pretraining can be costly.…”

Section: Related Work 21 Medical Question Answering and Data Augmenta...mentioning

confidence: 95%

DATLMedQA: A Data Augmentation and Transfer Learning Based Solution for Medical Question Answering

Zhou

Zhang

2021

Applied Sciences

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With the outbreak of COVID-19 that has prompted an increased focus on self-care, more and more people hope to obtain disease knowledge from the Internet. In response to this demand, medical question answering and question generation tasks have become an important part of natural language processing (NLP). However, there are limited samples of medical questions and answers, and the question generation systems cannot fully meet the needs of non-professionals for medical questions. In this research, we propose a BERT medical pretraining model, using GPT-2 for question augmentation and T5-Small for topic extraction, calculating the cosine similarity of the extracted topic and using XGBoost for prediction. With augmentation using GPT-2, the prediction accuracy of our model outperforms the state-of-the-art (SOTA) model performance. Our experiment results demonstrate the outstanding performance of our model in medical question answering and question generation tasks, and its great potential to solve other biomedical question answering challenges.

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“…Challenges including gaps in context retrieval and the GPT-4 model's inherent limitations regarding specialized biomedical data highlight the importance of developing specialized biomedical language models, fine-tuned with relevant data to bolster contextual understanding and response precision. [34][35][36][37] Other limitations relate to the precise safety guardrails that are appropriate for AI tools in general. While efforts were made to implement safety guardrails for AI responses, defining and enforcing these boundaries remains complex and proper constraint outside of drug dose recommendations can be much more challenging.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Empowering Personalized Pharmacogenomics with Generative AI Solutions

Murugan,

Yuan,

Venner

et al. 2024

Preprint

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Objective: This study evaluates an AI assistant developed using OpenAI's GPT-4 for interpreting pharmacogenomic (PGx) testing results, aiming to improve decision-making and knowledge sharing in clinical genetics, and to enhance patient care with equitable access. Methods: The AI assistant employs Retrieval Augmented Generation (RAG) combining retrieval and generative techniques. It employs a Knowledge Base (KB) comprising Clinical Pharmacogenetics Implementation Consortium (CPIC) data, with context-aware GPT-4 generating tailored responses to user queries from this KB, refined through prompt engineering and guardrails. Results: Evaluated against a specialized PGx question catalog, the AI assistant showed high efficacy in addressing user queries. Compared with OpenAI's ChatGPT 3.5, it demonstrated better performance, especially in provider-specific queries requiring specialized data and citations. Key areas for improvement include enhancing accuracy, relevancy, and representative language in responses. Discussion: The integration of context-aware GPT-4 with RAG significantly enhanced the AI assistant's utility. RAG's ability to incorporate domain-specific CPIC data, including recent literature, proved beneficial. Challenges persist, such as the need for specialized genetic/PGx models to improve accuracy and relevancy and addressing ethical, regulatory, and safety concerns. Conclusion: This study underscores generative AI's potential for transforming healthcare provider support and patient accessibility to complex pharmacogenomic information. While careful implementation of large language models like GPT-4 is necessary, it is clear that they can substantially improve understanding of pharmacogenomic data. With further development, these tools could augment healthcare expertise, provider productivity, and the delivery of equitable, patient-centered healthcare services.

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Named Entity Aware Transfer Learning for Biomedical Factoid Question Answering

Abstract: This is a repository copy of Named entity aware transfer learning for biomedical factoid question answering.

Cited by 19 publications

References 42 publications

Question answering systems for health professionals at the point of care—a systematic review

Question answering systems for health professionals at the point of care—a systematic review

DATLMedQA: A Data Augmentation and Transfer Learning Based Solution for Medical Question Answering

Empowering Personalized Pharmacogenomics with Generative AI Solutions

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