תובנה - Medical Informatics - # Radiology Report Information Extraction

Comprehensive Corpus of Annotated Medical Imaging Reports and Advanced Information Extraction Using BERT-based Language Models

Q: How can the CAMIR annotation schema and extraction models be extended to support other medical imaging modalities, such as radiographs, ultrasound, and mammography?

To extend the CAMIR annotation schema and extraction models to support other medical imaging modalities, such as radiographs, ultrasound, and mammography, several steps can be taken: Schema Expansion: The annotation schema can be expanded to include event types, triggers, and arguments specific to the new imaging modalities. For example, for mammography, additional events related to breast tissue findings can be included, while for ultrasound, events related to soft tissue abnormalities can be added. Anatomy Normalization: The anatomy normalization component of the schema can be updated to include specific anatomical structures relevant to radiographs, ultrasound, and mammography. This will ensure that the extracted information is consistent and can be easily compared across different modalities. Training Data Collection: New annotated datasets need to be created for the additional imaging modalities. These datasets should cover a diverse range of cases and findings to ensure the models are robust and generalizable. Model Training: The BERT-based extraction models can be retrained on the new annotated datasets to learn the patterns and relationships specific to the different imaging modalities. Fine-tuning the models on the new data will help them adapt to the nuances of each modality. Evaluation and Validation: The performance of the extended schema and models should be evaluated on test datasets specific to each modality. This will help assess the generalizability and effectiveness of the models across different imaging techniques. By following these steps, the CAMIR annotation schema and extraction models can be effectively extended to support a wider range of medical imaging modalities, enabling comprehensive information extraction from diverse types of radiology reports.

Q: What are the potential biases in the patient population represented in CAMIR, and how might they impact the generalizability of the extraction models?

Demographic Bias: The patient population in CAMIR may not be representative of the broader population, leading to demographic biases. For example, if the dataset predominantly includes reports from a specific age group or gender, the extraction models may not generalize well to more diverse patient populations. Disease Prevalence Bias: The distribution of medical conditions in CAMIR may not reflect the prevalence of diseases in other populations. This could lead to biases in the performance of the extraction models, especially when applied to datasets with different disease prevalence rates. Imaging Modality Bias: CAMIR focuses on specific imaging modalities, which could introduce biases when applying the extraction models to reports from other modalities. The models may not perform as well on modalities not adequately represented in the training data. Institutional Bias: The reports in CAMIR are from a single urban hospital system, which may introduce institutional biases. The language, terminology, and reporting practices in these reports may differ from those in reports from other institutions, impacting the generalizability of the extraction models. Impact on Generalizability: These biases can affect the generalizability of the extraction models to new datasets and institutions. Models trained on biased data may not perform well on diverse datasets, limiting their applicability in real-world settings. To mitigate these biases, it is essential to diversify the training data, include reports from multiple institutions and patient populations, and regularly evaluate the model performance on external datasets to ensure robustness and generalizability.

Q: How can the CAMIR corpus and extraction models be leveraged to support multimodal research, combining textual and visual information from radiology reports and associated medical images?

Integration of Image Data: The CAMIR corpus can be linked to image databases to create a multimodal dataset. This integration allows for the combination of textual information from radiology reports with visual data from medical images, enabling comprehensive analysis. Image-Text Fusion Models: Advanced models that can process both text and image data, such as multimodal transformers, can be trained using the CAMIR corpus. These models can learn the relationships between textual findings and corresponding images, enhancing the understanding of clinical information. Cross-Modal Retrieval: The extraction models can be extended to perform cross-modal retrieval, where textual queries from reports can retrieve relevant images and vice versa. This functionality aids in correlating findings in reports with visual representations. Clinical Decision Support: By leveraging multimodal research, the CAMIR corpus and extraction models can support clinical decision-making by providing a holistic view of patient cases. Integrating textual and visual information can enhance diagnostic accuracy and treatment planning. Validation and Interpretation: The multimodal approach allows for validation of findings across different modalities. Radiologists can use the combined information to validate their interpretations and make more informed decisions. By leveraging the CAMIR corpus and extraction models for multimodal research, healthcare professionals can benefit from a more comprehensive and integrated analysis of radiology reports and medical images, leading to improved patient care and outcomes.

מושגי ליבה

This study introduces a novel annotated corpus, CAMIR, which combines granular event-based annotations with concept normalization to comprehensively capture clinical findings from radiology reports. Two BERT-based information extraction models, mSpERT and PL-Marker++, are developed and evaluated on the CAMIR dataset, demonstrating performance comparable to human-level agreement.

תקציר

The authors present a novel annotated corpus called the Corpus of Annotated Medical Imaging Reports (CAMIR), which includes 609 radiology reports from Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography-Computed Tomography (PET-CT) modalities. The reports are annotated using a granular event-based schema that captures clinical indications, lesions, and medical problems, with most arguments normalized to predefined SNOMED-CT concepts.

The annotation process involved four medical students, with guidance from senior radiology experts. The corpus exhibits high inter-annotator agreement, exceeding 0.70 F1 for most argument types. Exceptions include Size Trend, Count, and Characteristic, which are relatively infrequent or linguistically diverse.

To extract the CAMIR events, the authors explored two BERT-based language models: mSpERT, which jointly extracts all event information, and PL-Marker++, a multi-stage approach that the authors augmented for the CAMIR schema. PL-Marker++ achieved the highest overall performance, significantly outperforming mSpERT, with an F1 score of 0.759 on the held-out test set.

The authors discuss the quality of the annotations, the model performance, and the validation of the span overlap evaluation criterion used. They also highlight the potential for CAMIR to support a wide range of secondary-use applications in the radiology domain, such as cohort discovery, epidemiology, image retrieval, automated follow-up tracking, computer-vision applications, decision support, and report summarization.

התאם אישית סיכום

כתוב מחדש עם AI

צור ציטוטים

תרגם מקור

לשפה אחרת

צור מפת חשיבה

מתוכן המקור

עבור למקור

arxiv.org

סטטיסטיקה

"Bilateral apical lung scarring" (Indication Anatomy)
"up to 5mm" (Lesion Size)
"multiple" (Lesion Count)
"New" (Lesion Size Trend)

ציטוטים

"CAMIR uniquely combines a granular event structure and concept normalization."
"PL-Marker++ achieved significantly higher overall performance than mSpERT (0.759 F1 vs 0.736 F1)."

תובנות מפתח מזוקקות מ:

A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models

by Namu Park,Ke... ב- arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.18975.pdf

A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models

שאלות מעמיקות

How can the CAMIR annotation schema and extraction models be extended to support other medical imaging modalities, such as radiographs, ultrasound, and mammography?

To extend the CAMIR annotation schema and extraction models to support other medical imaging modalities, such as radiographs, ultrasound, and mammography, several steps can be taken:

Schema Expansion: The annotation schema can be expanded to include event types, triggers, and arguments specific to the new imaging modalities. For example, for mammography, additional events related to breast tissue findings can be included, while for ultrasound, events related to soft tissue abnormalities can be added.

Anatomy Normalization: The anatomy normalization component of the schema can be updated to include specific anatomical structures relevant to radiographs, ultrasound, and mammography. This will ensure that the extracted information is consistent and can be easily compared across different modalities.

Training Data Collection: New annotated datasets need to be created for the additional imaging modalities. These datasets should cover a diverse range of cases and findings to ensure the models are robust and generalizable.

Model Training: The BERT-based extraction models can be retrained on the new annotated datasets to learn the patterns and relationships specific to the different imaging modalities. Fine-tuning the models on the new data will help them adapt to the nuances of each modality.

Evaluation and Validation: The performance of the extended schema and models should be evaluated on test datasets specific to each modality. This will help assess the generalizability and effectiveness of the models across different imaging techniques.

By following these steps, the CAMIR annotation schema and extraction models can be effectively extended to support a wider range of medical imaging modalities, enabling comprehensive information extraction from diverse types of radiology reports.

What are the potential biases in the patient population represented in CAMIR, and how might they impact the generalizability of the extraction models?

Demographic Bias: The patient population in CAMIR may not be representative of the broader population, leading to demographic biases. For example, if the dataset predominantly includes reports from a specific age group or gender, the extraction models may not generalize well to more diverse patient populations.

Disease Prevalence Bias: The distribution of medical conditions in CAMIR may not reflect the prevalence of diseases in other populations. This could lead to biases in the performance of the extraction models, especially when applied to datasets with different disease prevalence rates.

Imaging Modality Bias: CAMIR focuses on specific imaging modalities, which could introduce biases when applying the extraction models to reports from other modalities. The models may not perform as well on modalities not adequately represented in the training data.

Institutional Bias: The reports in CAMIR are from a single urban hospital system, which may introduce institutional biases. The language, terminology, and reporting practices in these reports may differ from those in reports from other institutions, impacting the generalizability of the extraction models.

Impact on Generalizability: These biases can affect the generalizability of the extraction models to new datasets and institutions. Models trained on biased data may not perform well on diverse datasets, limiting their applicability in real-world settings.

To mitigate these biases, it is essential to diversify the training data, include reports from multiple institutions and patient populations, and regularly evaluate the model performance on external datasets to ensure robustness and generalizability.

How can the CAMIR corpus and extraction models be leveraged to support multimodal research, combining textual and visual information from radiology reports and associated medical images?

Integration of Image Data: The CAMIR corpus can be linked to image databases to create a multimodal dataset. This integration allows for the combination of textual information from radiology reports with visual data from medical images, enabling comprehensive analysis.

Image-Text Fusion Models: Advanced models that can process both text and image data, such as multimodal transformers, can be trained using the CAMIR corpus. These models can learn the relationships between textual findings and corresponding images, enhancing the understanding of clinical information.

Cross-Modal Retrieval: The extraction models can be extended to perform cross-modal retrieval, where textual queries from reports can retrieve relevant images and vice versa. This functionality aids in correlating findings in reports with visual representations.

Clinical Decision Support: By leveraging multimodal research, the CAMIR corpus and extraction models can support clinical decision-making by providing a holistic view of patient cases. Integrating textual and visual information can enhance diagnostic accuracy and treatment planning.

Validation and Interpretation: The multimodal approach allows for validation of findings across different modalities. Radiologists can use the combined information to validate their interpretations and make more informed decisions.

By leveraging the CAMIR corpus and extraction models for multimodal research, healthcare professionals can benefit from a more comprehensive and integrated analysis of radiology reports and medical images, leading to improved patient care and outcomes.