Large Language Models are Few-Shot Clinical Information Extractors

Agrawal, Monica; Hegselmann, Stefan; Lang, Hunter; Kim, Yoon; Sontag, David

doi:10.48550/arxiv.2205.12689

Cited by 22 publications

(25 citation statements)

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“…However, they pre-train GPT on very limited amount of data (only 0.5M PubMed abstracts) and use it only for data-augmentation for relation extraction task. A recent work on using GPT model is [22], where they design converters for GPT-3 [7] for several unconventional downstream clinical tasks.…”

Section: Pre-trained Language Models In Biomedical Domainmentioning

confidence: 99%

BioGPT: generative pre-trained transformer for biomedical text generation and mining

Luo

Sun

Xia

et al. 2022

Briefings in Bioinformatics

385

159

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Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.

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Section: Pre-trained Language Models In Biomedical Domainmentioning

confidence: 99%

BioGPT: generative pre-trained transformer for biomedical text generation and mining

Luo

Sun

Xia

et al. 2022

Briefings in Bioinformatics

385

159

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“…Commands can also combine both elements, like "get 3 seconds where the dog sits on the chair". This functionality harnesses the LLM's information extraction capability [7] to identify segments aligning with user descriptions. For transparency, the LLM also explains its rationale for the trimmings, detailing how they align with user instructions.…”

Section: Clip Trimmingmentioning

confidence: 99%

LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing

Wang,

Li,

et al. 2024

Proceedings of the 29th International Conference on Intelligent User Interfaces

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“…ChatGPT is a chatbot with remarkable conversation capacity and astonishing human-like text generation ability, approaching the possibility of artificial general intelligence. Additionally, LLMs have great promise at atomic-level protein structure design, [327] digital clinical information extraction, [328] applications in medicine and healthcare. [329,330] The revolutionary role of LLMs trained on troves of data in both academia and industry have altered the way human beings interact with machines in various aspects.…”

Section: Large Raman Modelsmentioning

confidence: 99%

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Bi,

Lin,

Chen

et al. 2023

Small Methods

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Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever‐sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high‐level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above‐mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

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Large Language Models are Few-Shot Clinical Information Extractors

Cited by 22 publications

References 0 publications

BioGPT: generative pre-trained transformer for biomedical text generation and mining

BioGPT: generative pre-trained transformer for biomedical text generation and mining

LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

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