аналитика - Biomedical Informatics - # Relation Extraction with LLMs

High-throughput Biomedical Relation Extraction with Large Language Models

Q: How can the proposed framework be adapted for other types of relations beyond manifestation, diagnosis, and treatment?

The proposed framework for biomedical relation extraction can be adapted for other types of relations by expanding the semantic categories and relation types in the classification task. To adapt the framework for additional relation types, the first step would involve identifying and defining the new types of relations that need to be extracted. This could include categories such as causation, association, or interaction between different biomedical entities. Once the new relation types are defined, the framework can be modified to incorporate these categories into the binary classification task. This would involve updating the prompt structure to include prompts specific to the new relation types and adjusting the reasoning mechanisms to provide explanations for the decisions made by the LLMs. Furthermore, the data preprocessing step would need to be expanded to include additional terms and entities relevant to the new relation types. This may involve updating the biomedical thesaurus used for term matching and retrieval to encompass a broader range of entities and relationships. Overall, by customizing the prompt structure, updating the classification task, and expanding the data preprocessing steps, the framework can be adapted to extract a wider variety of biomedical relations beyond manifestation, diagnosis, and treatment.

Q: What are the limitations of relying solely on LLMs for relation extraction in biomedical contexts?

While LLMs offer significant advantages in biomedical relation extraction, there are limitations to relying solely on these models for this task. Some of the key limitations include: Hallucination: LLMs may generate incorrect or misleading information, especially when faced with complex or ambiguous contexts. This can lead to the extraction of inaccurate relation triplets, impacting the overall quality of the extracted knowledge. Limited Context Understanding: LLMs have constraints on the length of the context they can process, which may result in important information being overlooked or not fully considered in the relation extraction process. This limitation can affect the accuracy and completeness of the extracted relations. Domain-specific Knowledge: Biomedical contexts often require specialized domain knowledge to interpret and extract relations accurately. LLMs may lack the specific biomedical expertise needed to understand complex medical terms, concepts, and relationships, leading to errors in extraction. Interpretability: LLMs provide outputs without detailed explanations of their reasoning, making it challenging to understand why a specific decision was made. This lack of interpretability can hinder the validation and trustworthiness of the extracted relations. Generalization: LLMs trained on a diverse range of data may struggle with generalizing to specific biomedical contexts, especially when dealing with rare or specialized medical conditions or treatments. This can result in suboptimal performance in relation extraction tasks.

Q: How can the use of LLMs in biomedical relation extraction impact the development of knowledge repositories in the field?

The use of LLMs in biomedical relation extraction can have a significant impact on the development of knowledge repositories in the field by: Automating Data Extraction: LLMs can automate the extraction of complex biomedical relations from large volumes of text, enabling the rapid creation of structured knowledge repositories. This automation accelerates the process of knowledge acquisition and curation. Enhancing Data Quality: LLMs can improve the quality of extracted relations by leveraging their reading comprehension abilities and world knowledge. This leads to more accurate and reliable information being added to knowledge repositories, enhancing their value for research and clinical applications. Enabling Scalability: LLMs enable high-throughput relation extraction, allowing for the extraction of a vast number of relations across diverse biomedical websites and sources. This scalability facilitates the creation of comprehensive and up-to-date knowledge repositories. Facilitating Research and Innovation: By extracting and organizing complex biomedical relations, LLMs contribute to the advancement of research and innovation in the field. Knowledge repositories enriched with LLM-extracted relations provide valuable insights for researchers, clinicians, and other stakeholders. Supporting Clinical Decision Making: Knowledge repositories enriched with LLM-extracted relations can serve as valuable resources for clinical decision support systems. By providing structured and accurate biomedical information, these repositories can assist healthcare professionals in making informed decisions for patient care.

Основные понятия

Developing a high-throughput biomedical relation extraction system using large language models for semi-structured web articles.

Аннотация

Authors: Songchi Zhou, Sheng Yu
Affiliation: Tsinghua University, Beijing, China
Objective: Develop a system for biomedical relation extraction using LLMs.
Methods: Formulate relation extraction as binary classifications for LLMs.
Results: Extracted 248,659 relation triplets from reputable biomedical websites.
Conclusion: Demonstrated effectiveness in leveraging LLMs for relation extraction.

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Статистика

Using an open-source LLM, we extracted 248659 relation triplets of three distinct relation types from three reputable biomedical websites.
Evaluation results indicate that the pipeline exhibits performance comparable to that of GPT-4.
The proposed method has demonstrated its effectiveness in leveraging the strengths of LLMs for high-throughput biomedical relation extraction.

Цитаты

"Employment is mentioned as a factor in describing some of the behaviors of a person with Antisocial Personality Disorder." - GPT-4
"Prophylaxis refers to the preventive measure taken against Plague, which includes prescribed medications like doxycycline and ciprofloxacin." - SOLAR 70B

Ключевые выводы из

High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models

by Songchi Zhou... в arxiv.org 03-27-2024

https://arxiv.org/pdf/2312.08274.pdf

High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models

Дополнительные вопросы

How can the proposed framework be adapted for other types of relations beyond manifestation, diagnosis, and treatment?

The proposed framework for biomedical relation extraction can be adapted for other types of relations by expanding the semantic categories and relation types in the classification task. To adapt the framework for additional relation types, the first step would involve identifying and defining the new types of relations that need to be extracted. This could include categories such as causation, association, or interaction between different biomedical entities.
Once the new relation types are defined, the framework can be modified to incorporate these categories into the binary classification task. This would involve updating the prompt structure to include prompts specific to the new relation types and adjusting the reasoning mechanisms to provide explanations for the decisions made by the LLMs.
Furthermore, the data preprocessing step would need to be expanded to include additional terms and entities relevant to the new relation types. This may involve updating the biomedical thesaurus used for term matching and retrieval to encompass a broader range of entities and relationships.
Overall, by customizing the prompt structure, updating the classification task, and expanding the data preprocessing steps, the framework can be adapted to extract a wider variety of biomedical relations beyond manifestation, diagnosis, and treatment.

What are the limitations of relying solely on LLMs for relation extraction in biomedical contexts?

While LLMs offer significant advantages in biomedical relation extraction, there are limitations to relying solely on these models for this task. Some of the key limitations include:

Hallucination: LLMs may generate incorrect or misleading information, especially when faced with complex or ambiguous contexts. This can lead to the extraction of inaccurate relation triplets, impacting the overall quality of the extracted knowledge.

Limited Context Understanding: LLMs have constraints on the length of the context they can process, which may result in important information being overlooked or not fully considered in the relation extraction process. This limitation can affect the accuracy and completeness of the extracted relations.

Domain-specific Knowledge: Biomedical contexts often require specialized domain knowledge to interpret and extract relations accurately. LLMs may lack the specific biomedical expertise needed to understand complex medical terms, concepts, and relationships, leading to errors in extraction.

Interpretability: LLMs provide outputs without detailed explanations of their reasoning, making it challenging to understand why a specific decision was made. This lack of interpretability can hinder the validation and trustworthiness of the extracted relations.

Generalization: LLMs trained on a diverse range of data may struggle with generalizing to specific biomedical contexts, especially when dealing with rare or specialized medical conditions or treatments. This can result in suboptimal performance in relation extraction tasks.

How can the use of LLMs in biomedical relation extraction impact the development of knowledge repositories in the field?

The use of LLMs in biomedical relation extraction can have a significant impact on the development of knowledge repositories in the field by:

Automating Data Extraction: LLMs can automate the extraction of complex biomedical relations from large volumes of text, enabling the rapid creation of structured knowledge repositories. This automation accelerates the process of knowledge acquisition and curation.

Enhancing Data Quality: LLMs can improve the quality of extracted relations by leveraging their reading comprehension abilities and world knowledge. This leads to more accurate and reliable information being added to knowledge repositories, enhancing their value for research and clinical applications.

Enabling Scalability: LLMs enable high-throughput relation extraction, allowing for the extraction of a vast number of relations across diverse biomedical websites and sources. This scalability facilitates the creation of comprehensive and up-to-date knowledge repositories.

Facilitating Research and Innovation: By extracting and organizing complex biomedical relations, LLMs contribute to the advancement of research and innovation in the field. Knowledge repositories enriched with LLM-extracted relations provide valuable insights for researchers, clinicians, and other stakeholders.

Supporting Clinical Decision Making: Knowledge repositories enriched with LLM-extracted relations can serve as valuable resources for clinical decision support systems. By providing structured and accurate biomedical information, these repositories can assist healthcare professionals in making informed decisions for patient care.