The paper aims to address the domain-specific knowledge gap that general-purpose language models encounter when dealing with clinical or specialized text . This is not a new problem, as existing language models face challenges in understanding the nuances and complexities of specialized domains like clinical information .

This paper aims to validate the scientific hypothesis that incorporating external knowledge to align and update internal knowledge improves the quality of the method's generated rules in the context of automated clinical data extraction . The study demonstrates that utilizing domain knowledge enhances the performance of the model, particularly in fields related to lesions, with improvements in lesion size, margin, and solidity . Additionally, the paper explores the impact of different components, such as extended context, controlled vocabulary, and knowledge alignment, on the extraction of lesion descriptions, highlighting the importance of these elements in preventing error propagation and standardizing the extraction process .

The paper proposes a novel framework for the extraction of lung lesion information from clinical and imaging reports using Large Language Models (LLMs) . This framework aligns internal knowledge with external knowledge through in-context learning (ICL) to enhance the reliability and accuracy of extracted information . The approach involves a retriever to identify relevant internal or external knowledge units and a grader to evaluate the truthfulness and helpfulness of the retrieved internal-knowledge rules, aligning and updating the knowledge bases .

One key aspect of the proposed method is the two-stage extraction process. The first stage focuses on lung lesion finding detection and primary structured field parsing, while the second stage involves further parsing of lesion description text into additional structured fields . In the first stage, the model uses the retrieved internal knowledge along with task instructions, input reports, and few-shot samples for lesion finding detection . The second stage utilizes a controlled vocabulary based on the SNOMED ontology to extract additional structured fields from the lesion description text .

The paper introduces the concept of aligning and updating internal knowledge with external knowledge through in-context learning (ICL) to improve the accuracy and reliability of LLM outputs . This approach addresses the extraction task in two stages, enhancing the F1 score for key fields such as lesion size, margin, and solidity . By dynamically selecting and updating internal knowledge and using external knowledge solely for internal-knowledge updates, the method aims to mitigate current LLM limitations like hallucination and increase the quality of generated rules .

Furthermore, the paper emphasizes the importance of incorporating domain-specific knowledge to improve the performance of clinical information extraction systems . It discusses the evolution from rule-based and dictionary-based approaches to more powerful machine learning techniques and deep learning models for clinical information extraction . The proposed method leverages LLMs to extract multiple fields simultaneously without requiring labeled training data for each field, demonstrating potential for accelerating clinical data extraction . The proposed framework for automated clinical data extraction with knowledge-conditioned Large Language Models (LLMs) offers several key characteristics and advantages compared to previous methods .

1. Alignment of Internal and External Knowledge: The method aligns and updates internal knowledge with external sources through in-context learning (ICL) to enhance the reliability and accuracy of extracted information . This alignment process significantly improves the quality of generated rules, leading to a 12.9% increase in F1 score across all fields compared to existing ICL baselines .

2. Two-Stage Extraction Process: The framework divides the extraction task into two stages: detection and parsing . In the first stage, the model leverages internal knowledge, task instructions, input reports, and few-shot samples for lesion finding detection, while the second stage focuses on further parsing of lesion description text into structured fields . This two-stage approach enhances the accuracy of key fields such as lesion size, margin, and solidity .

3. Incorporation of Domain-Specific Knowledge: Unlike existing language models that primarily focus on general domain knowledge, this framework addresses the domain-specific knowledge gap by incorporating external knowledge sources such as knowledge bases, ontologies, and domain-specific corpora . By providing domain-specific knowledge, the model can better understand and reason about specialized domains, improving performance in clinical text extraction tasks .

4. Iterative Refinement and Knowledge Alignment: The model iteratively updates internal knowledge based on a grader's assessment of rule truthfulness, ensuring that the extracted rules align with the specific extraction task . This iterative refinement process enhances the consistency and reduces noise in the extracted information, leading to more accurate results .

5. Enhanced Extraction Accuracy: The framework outperforms commonly used ICL methods and Reference-Aided Generation (RAG) techniques, particularly excelling in accurately detecting and extracting clinically important fields like lesion size, margin, and solidity . By dynamically selecting and updating internal knowledge and using external knowledge solely for internal-knowledge updates, the method achieves significant improvements in accuracy and mitigates current LLM limitations like hallucination .

Overall, the proposed framework offers a comprehensive and effective approach to automated clinical data extraction by leveraging knowledge-conditioned LLMs, aligning internal and external knowledge, and incorporating domain-specific information to enhance extraction accuracy and reliability .

Could you please specify the topic or field you are referring to so I can provide you with more accurate information?

The experiments in the paper were designed with a focus on automated clinical data extraction using a two-stage framework for lung lesion extraction . The experiments involved utilizing a curated dataset from a real-world clinical trial with annotations from medical experts . The goal was to extract lung lesion findings from clinical and imaging reports, including diagnostic imaging reports from CT scans and clinical reports, with key fields such as lesion size, margin, solidity, lobe, and standardized uptake value (SUV) . The methodology included a two-stage clinical data extraction framework that incorporated internal and external knowledge bases to improve the accuracy of lung lesion clinical data extractions . The experiments also evaluated the performance of the system against baseline methods, showcasing improvements in accuracy compared to existing in-context learning (ICL) methods .

The dataset used for quantitative evaluation in the study is from real-world clinical trials . However, the information regarding whether the code is open source is not explicitly mentioned in the provided context.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study introduces a novel framework that aligns internal and external knowledge through in-context learning (ICL) to enhance the accuracy and reliability of Large Language Models (LLMs) in extracting lung lesion information from clinical and medical imaging reports . The experiments demonstrate that this knowledge-conditioned approach significantly improves the F1 score for key fields such as lesion size, margin, and solidity by an average of 12.9% compared to existing ICL methods .

Furthermore, the study evaluates the performance of the model using precision, recall, and F1 scores, showing the effectiveness of the approach in accurately extracting critical clinical information from reports . The results indicate that the model's ability to extract important fields like lesion size, margin, and solidity, which are crucial for cancer work, is notably enhanced by incorporating external knowledge .

Overall, the experiments and results in the paper provide strong empirical evidence supporting the effectiveness of the proposed framework in improving the accuracy and reliability of LLM outputs for the extraction of lung lesion information from clinical and medical reports, thereby validating the scientific hypotheses put forth in the study .

The paper "Automated Clinical Data Extraction with Knowledge Conditioned LLMs" proposes a novel framework that aligns internal and external knowledge through in-context learning (ICL) to improve the accuracy and reliability of Large Language Models (LLMs) in interpreting unstructured text in clinical and medical imaging reports . This framework involves a retriever to identify relevant knowledge units, a grader to evaluate the retrieved knowledge, and a process to align and update knowledge bases . The approach enhances LLM outputs by addressing the extraction task in two stages: first detecting lung lesions and parsing structured fields, then further parsing lesion descriptions into additional structured fields . The experiments conducted with expert-curated datasets show that this ICL approach can increase the F1 score for key fields by an average of 12.9% compared to existing methods .

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