Self-tracking can help personalize self-management interventionsfor chronic conditions like type 2 diabetes (T2D), but refecting onpersonal data requires motivation and literacy. Machine learning(ML) methods can ident...
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Objective Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of...
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Objective Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional deep learning models are adept at learning intricate feature representations and dependencies in sequential data, but often require extensive labeled data. In contrast, large language models (LLMs) excel in understanding contextual information, but exhibit unstable performance on named entity recognition (NER) tasks, possibly due to their broad but unspecific training. This study aims to evaluate the effectiveness of LLMs and traditional deep learning models in AE extraction, and to assess the impact of ensembling these models on performance. Methods In this study, we utilized reports and posts from the Vaccine Adverse Event Reporting System (VAERS) (n=621), Twitter (n=9,133), and Reddit (n=131) as our corpora. Our goal was to extract three types of entities: vaccine, shot, and adverse event (ae). We explored and fine-tuned (except GPT-4) multiple LLMs, including GPT-2, GPT-3.5, GPT-4, Llama-2 7b, and Llama-2 13b, as well as traditional deep learning models like Recurrent neural network (RNN) and Bidirectional Encoder Representations from Transformers for biomedical Text Mining (BioBERT). To enhance performance, we created ensembles of the three models with the best performance. For evaluation, we used strict and relaxed F1 scores to evaluate the performance for each entity type, and micro-average F1 was used to assess the overall performance. Results The ensemble model achieved the highest performance in "vaccine," "shot," and "ae," with strict F1-scores of 0.878, 0.930, and 0.925, respectively, along with a micro-average score of 0.903. These results underscore the significance of fine-tuning models for specific tasks and demonstrate the effectiveness of ensemble methods in enhancing performance. Conclusion In conclusion,
Recently, computational drug repurposing has emerged as a promising method for identifying new interventions for diseases. This study predicts novel drugs for Alzheimer's disease (AD) through link prediction on ou...
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Medical practice in the intensive care unit is based on the assumption that physiological systems such as the human glucose-insulin system are predictable. We demonstrate that delay within the glucose-insulin system c...
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Electronic Health Records (EHRs) represent a crucial data source for real-world evidence generation. To facilitate biomedical studies using EHRs, standard data models like the OMOP CDM have been developed. Nevertheles...
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ISBN:
(数字)9798350383737
ISBN:
(纸本)9798350383744
Electronic Health Records (EHRs) represent a crucial data source for real-world evidence generation. To facilitate biomedical studies using EHRs, standard data models like the OMOP CDM have been developed. Nevertheless, recent advancements in biomedical AI research that leverage EHRs have introduced new challenges, encompassing security considerations, large-scale data retrieval, and computational resource management, including GPUs. This paper introduces Kamino, an innovative architectural solution tailored to support biomedical AI research using EHR data. Kamino offers a user-friendly interface with features designed for efficient team access management in accordance with regulatory requirements. It facilitates direct data retrieval from an OMOP CDM instance and includes a resource allocation system based on Kubernetes orchestration. Here, we demonstrate the practical application and utility of Kamino through a clinical natural language processing task. We firmly believe that such a tool will significantly expedite AI research conducted with EHR data within academic institutions.
In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers ...
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Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains;however, these models encounter issues such as generating inaccurate information...
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Retrieval-augmented generation (RAG) offers a solution by retrieving knowledge from an established database to avoid hallucination of large language models (LLM). However, these models utilize specialized cross-attent...
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In “How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge” (MIR Med Educ 2023;9:e45312) three additions we...
This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like t...
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