A Multimodal Pipeline for Clinical Data Extraction: Applying Vision-Language Models to Scans of Transfusion Reaction Reports
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916712330297344 |
|---|---|
| author | Schäfer, Henning Schmidt, Cynthia S. Wutzkowsky, Johannes Lorek, Kamil Reinartz, Lea Rückert, Johannes Temme, Christian Böckmann, Britta Horn, Peter A. Friedrich, Christoph M. |
| author_facet | Schäfer, Henning Schmidt, Cynthia S. Wutzkowsky, Johannes Lorek, Kamil Reinartz, Lea Rückert, Johannes Temme, Christian Böckmann, Britta Horn, Peter A. Friedrich, Christoph M. |
| contents | Despite the growing adoption of electronic health records, many processes still rely on paper documents, reflecting the heterogeneous real-world conditions in which healthcare is delivered. The manual transcription process is time-consuming and prone to errors when transferring paper-based data to digital formats. To streamline this workflow, this study presents an open-source pipeline that extracts and categorizes checkbox data from scanned documents. Demonstrated on transfusion reaction reports, the design supports adaptation to other checkbox-rich document types. The proposed method integrates checkbox detection, multilingual optical character recognition (OCR) and multilingual vision-language models (VLMs). The pipeline achieves high precision and recall compared against annually compiled gold-standards from 2017 to 2024. The result is a reduction in administrative workload and accurate regulatory reporting. The open-source availability of this pipeline encourages self-hosted parsing of checkbox forms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20220 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multimodal Pipeline for Clinical Data Extraction: Applying Vision-Language Models to Scans of Transfusion Reaction Reports Schäfer, Henning Schmidt, Cynthia S. Wutzkowsky, Johannes Lorek, Kamil Reinartz, Lea Rückert, Johannes Temme, Christian Böckmann, Britta Horn, Peter A. Friedrich, Christoph M. Computation and Language Computer Vision and Pattern Recognition 68T07 I.7.5; I.4.7; I.2.7; H.3.3; J.3 Despite the growing adoption of electronic health records, many processes still rely on paper documents, reflecting the heterogeneous real-world conditions in which healthcare is delivered. The manual transcription process is time-consuming and prone to errors when transferring paper-based data to digital formats. To streamline this workflow, this study presents an open-source pipeline that extracts and categorizes checkbox data from scanned documents. Demonstrated on transfusion reaction reports, the design supports adaptation to other checkbox-rich document types. The proposed method integrates checkbox detection, multilingual optical character recognition (OCR) and multilingual vision-language models (VLMs). The pipeline achieves high precision and recall compared against annually compiled gold-standards from 2017 to 2024. The result is a reduction in administrative workload and accurate regulatory reporting. The open-source availability of this pipeline encourages self-hosted parsing of checkbox forms. |
| title | A Multimodal Pipeline for Clinical Data Extraction: Applying Vision-Language Models to Scans of Transfusion Reaction Reports |
| topic | Computation and Language Computer Vision and Pattern Recognition 68T07 I.7.5; I.4.7; I.2.7; H.3.3; J.3 |
| url | https://arxiv.org/abs/2504.20220 |