CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice

Raza, Shaina; Schwartz, Brian; Rosella, Laura

doi:10.1186/s12859-022-04751-6

Cited by 20 publications

(16 citation statements)

References 52 publications

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“…CORD-19 was also used for the training of CoQUAD, a question-answering system, designed to find the most recent evidence and answer any related questions ( 30 ).…”

Section: Large Language Models To Support Academic Researchmentioning

confidence: 99%

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health

Angelis

Baglivo

Arzilli

et al. 2023

Front. Public Health

247

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Large Language Models (LLMs) have recently gathered attention with the release of ChatGPT, a user-centered chatbot released by OpenAI. In this perspective article, we retrace the evolution of LLMs to understand the revolution brought by ChatGPT in the artificial intelligence (AI) field.The opportunities offered by LLMs in supporting scientific research are multiple and various models have already been tested in Natural Language Processing (NLP) tasks in this domain.The impact of ChatGPT has been huge for the general public and the research community, with many authors using the chatbot to write part of their articles and some papers even listing ChatGPT as an author. Alarming ethical and practical challenges emerge from the use of LLMs, particularly in the medical field for the potential impact on public health. Infodemic is a trending topic in public health and the ability of LLMs to rapidly produce vast amounts of text could leverage misinformation spread at an unprecedented scale, this could create an “AI-driven infodemic,” a novel public health threat. Policies to contrast this phenomenon need to be rapidly elaborated, the inability to accurately detect artificial-intelligence-produced text is an unresolved issue.

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“…CORD-19 was also used for the training of CoQUAD, a question-answering system, designed to find the most recent evidence and answer any related questions ( 30 ).…”

Section: Large Language Models To Support Academic Researchmentioning

confidence: 99%

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health

Angelis

Baglivo

Arzilli

et al. 2023

Front. Public Health

247

View full text Add to dashboard Cite

show abstract

“…Additionally, these systems can help to reduce medical errors and improve patient outcomes. By providing recommendations based on evidence-based medicine and clinical guidelines, clinicians can be more confident in their treatment decisions and provide better care to their patients [36], [37].…”

Section: Practical Impactmentioning

confidence: 99%

Improving Clinical Decision Making with a Two-Stage Recommender System: A Case Study on MIMIC-III Dataset

Raza

2023

Preprint

Self Cite

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Clinical decision-making is a challenging and time-consuming task that involves integrating a vast amount of patient data, including medical history, test results, and notes from clinicians. To assist this process, clinical recommender systems have been developed to provide personalized recommendations to healthcare practitioners. However, creating effective clinical recommender systems is complex due to the diversity and intricacy of clinical data and the need for customized recommendations. In this paper, we propose a two-stage recommender framework for clinical decision-making basedon the publicly available MIMIC dataset of electronic health records. The first stage of the framework employs a deep neural networkbased model to retrieve a set of candidate items, such as diagnosis, medication, and prescriptions, from the patient’s electronic health records. The model is trained to extract relevant information from clinical notes using a pre-trained language model. The second stage of the framework utilizes a deep learning model to rank and recommend the most pertinent items to healthcare providers. The model considers the patient’s medical history and the context of the current visit to offer personalized recommendations. To evaluate the proposed model, we compared it to various baseline models using multiple evaluation metrics. The findings indicate that the proposed model achieved a precision of 89% and a macro-average F1 score of approximately 84%, indicating its potential to improve clinical decision-making and reduce information overload for healthcare providers. The paper also discusses challenges, such as data availability, privacy, and bias, and suggests areas for future research in this field.

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“…Such enormous data is challenging to process, significantly when the urgency of time and the number of patients is increasing exponentially. [ 18 ]. To perform biomedical mining, it is essential to accommodate a prior process of biomedical NER.…”

Section: Related Workmentioning

confidence: 99%

Large-scale application of named entity recognition to biomedicine and epidemiology

Raza

Reji²,

Shajan³

et al. 2022

PLOS Digit Health

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Background Despite significant advancements in biomedical named entity recognition methods, the clinical application of these systems continues to face many challenges: (1) most of the methods are trained on a limited set of clinical entities; (2) these methods are heavily reliant on a large amount of data for both pre-training and prediction, making their use in production impractical; (3) they do not consider non-clinical entities, which are also related to patient’s health, such as social, economic or demographic factors. Methods In this paper, we develop Bio-Epidemiology-NER (https://pypi.org/project/Bio-Epidemiology-NER/) an open-source Python package for detecting biomedical named entities from the text. This approach is based on a Transformer-based system and trained on a dataset that is annotated with many named entities (medical, clinical, biomedical, and epidemiological). This approach improves on previous efforts in three ways: (1) it recognizes many clinical entity types, such as medical risk factors, vital signs, drugs, and biological functions; (2) it is easily configurable, reusable, and can scale up for training and inference; (3) it also considers non-clinical factors (age and gender, race and social history and so) that influence health outcomes. At a high level, it consists of the phases: pre-processing, data parsing, named entity recognition, and named entity enhancement. Results Experimental results show that our pipeline outperforms other methods on three benchmark datasets with macro-and micro average F1 scores around 90 percent and above. Conclusion This package is made publicly available for researchers, doctors, clinicians, and anyone to extract biomedical named entities from unstructured biomedical texts.

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CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice

Cited by 20 publications

References 52 publications

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health

Improving Clinical Decision Making with a Two-Stage Recommender System: A Case Study on MIMIC-III Dataset

Large-scale application of named entity recognition to biomedicine and epidemiology

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