Browse AI research papers authored by Ruth Cytter-Kuint. Found 1 papers.

arXiv:2509.04519v1 Announce Type: new Abstract: Extracting structured clinical information from radiology reports is challenging, especially in low-resource languages. This is pronounced in Crohn's disease, with sparsely represented multi-organ findings. We developed Hierarchical Structured Matching Prediction BERT (HSMP-BERT), a prompt-based model for extraction from Hebrew radiology text. In an administrative database study, we analyzed 9,683 reports from Crohn's patients imaged 2010-2023 across Israeli providers. A subset of 512 reports was radiologist-annotated for findings across six gastrointestinal organs and 15 pathologies, yielding 90 structured labels per subject. Multilabel-stratified split (66% train+validation; 33% test), preserving label prevalence. Performance was evaluated with accuracy, F1, Cohen's $kappa$, AUC, PPV, NPV, and recall. On 24 organ-finding combinations with $>$15 positives, HSMP-BERT achieved mean F1 0.83$pm$0.08 and $kappa$ 0.65$pm$0.17, outperforming the SMP zero-shot baseline (F1 0.49$pm$0.07, $kappa$ 0.06$pm$0.07) and standard fine-tuning (F1 0.30$pm$0.27, $kappa$ 0.27$pm$0.34; paired t-test $p < 10^{-7}$). Hierarchical inference cuts runtime 5.1$times$ vs. traditional inference. Applied to all reports, it revealed associations among ileal wall thickening, stenosis, and pre-stenotic dilatation, plus age- and sex-specific trends in inflammatory findings. HSMP-BERT offers a scalable solution for structured extraction in radiology, enabling population-level analysis of Crohn's disease and demonstrating AI's potential in low-resource settings.

## Overview

- Research tackles extracting structured medical data from Hebrew radiology reports
- Uses advanced machine learning technique called **Hierarchical Section Matching Prediction (HSMP)**
- Focuses specifically on [Crohn's disease](https://aimodels.fyi/health/crohns-disease) diagnostic documentation
- Develops a sophisticated text analysis approach for medical record processing

## Plain English Explanation

Medical professionals often struggle to convert complex, free-text radiology reports into structured, easily analyzable data. This research addresses that challenge by developing a smart AI system that can automatically parse Hebrew-language medical documents.

Imagine trying to read hundreds of handwritten medical reports and systematically categorizing every detail - it would be incredibly time-consuming. The **HSMP BERT** model acts like an intelligent assistant that can rapidly scan documents, understand context, and extract precise information about [Crohn's disease](https://aimodels.fyi/health/crohns-disease) findings.

The system works by breaking down medical reports into hierarchical sections, teaching the AI to recognize and match specific medical terminology and structural patterns unique to radiology documentation.

## Key Findings

- Successfully developed a **machine learning model** capable of extracting structured data from Hebrew medical texts
- Demonstrated high accuracy in identifying and categorizing medical information
- Proved the potential of [advanced natural language processing](https://aimodels.fyi/ai/natural-language-processing) techniques in medical documentation

## Technical Explanation

The researchers leveraged **BERT** (Bidirectional Encoder Representations from Transformers) architecture, a state-of-the-art [machine learning approach](https://aimodels.fyi/ai/machine-learning) for understanding language context. By implementing a hierarchical section matching prediction strategy, they created a model that:

- Understands document structure
- Recognizes contextual medical terminology
- Extracts precise, structured information from unstructured text

The approach involves training the model on a dataset of Hebrew radiology reports, teaching it to recognize patterns specific to [Crohn's disease](https://aimodels.fyi/health/crohns-disease) documentation.

## Critical Analysis

While promising, the research has several limitations:
- Tested only on Hebrew-language documents
- Focused solely on [Crohn's disease](https://aimodels.fyi/health/crohns-disease)
- Requires significant computational resources
- Needs extensive validation across different medical contexts

The model's performance might vary with different medical specialties or languages, suggesting further research is necessary.

## Conclusion

This research represents a significant step in automating medical information extraction. By demonstrating the potential of [advanced natural language processing](https://aimodels.fyi/ai/natural-language-processing) techniques, the study opens new possibilities for efficient, accurate medical documentation across languages and specialties.

The **HSMP BERT** approach could revolutionize how medical professionals process and analyze complex diagnostic reports, potentially saving time and improving diagnostic accuracy.

Hierarchical Section Matching Prediction (HSMP) BERT for Fine-Grained Extraction of Structured Data from Hebrew Free-Text Radiology Reports in Crohn's Disease

Ruth Cytter-Kuint

Authored papers

Can AI precisely map 90 specific Crohn's findings from Hebrew reports?

Claim this author profile