{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T23:15:28Z","timestamp":1780787728149,"version":"3.54.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686158","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,3]]},"abstract":"<jats:p>Introduction: Manual ICD-10 coding of German clinical texts is time-consuming and error-prone. This project aims to develop a semi-automated pipeline for efficient coding of unstructured medical documentation. State of the art: Existing approaches often rely on fine-tuned language models that require large datasets and perform poorly on rare codes, particularly in low-resource languages such as German. Concept: The proposed system integrates Named Entity Recognition, semantic and lexical retrieval, abbreviation resolution, and context-aware normalization within a Retrieval-Augmented Generation (RAG) framework using a compact generative model. Implementation: The pipeline utilizes Sentence-BERT embeddings, FAISS indexing, and the Mistral-Small-Instruct model. ICD codes are assigned through a combination of semantic similarity and generative refinement among the top retrieval candidates. Lessons learned: Major sources of error were found in semantic retrieval and diagnosis normalization. Future improvements should focus on domain-specific German embeddings, more robust abbreviation handling, and enhanced context-aware prompting to increase accuracy and usability in clinical environments.<\/jats:p>","DOI":"10.3233\/shti251397","type":"book-chapter","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T10:24:47Z","timestamp":1756895087000},"source":"Crossref","is-referenced-by-count":1,"title":["Retrieval-Augmented Generation for ICD-10 Coding in German Clinical Texts \u2013 A Technical Case Report"],"prefix":"10.3233","author":[{"given":"Mario","family":"Krumscheid","sequence":"first","affiliation":[{"name":"Department of Computer Science, Kempten University of Applied Sciences, Kempten, Germany"},{"name":"Bavarian Center for Digital Health and Social Care, Kempten, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Bl\u00f6mer","sequence":"additional","affiliation":[{"name":"Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Becker","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Kempten University of Applied Sciences, Kempten, Germany"},{"name":"Bavarian Center for Digital Health and Social Care, Kempten, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2025: GMDS Illuminates Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251397","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T10:24:47Z","timestamp":1756895087000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251397"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,3]]},"ISBN":["9781643686158"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251397","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,3]]}}}