Automated Phenotype Annotation in Spanish Clinical Text: A Large Language Model Approach with Retrieval-Augmented Generation

This work addresses the challenge of automatically annotating phenotypes from unstructured Spanish clinical text. Phenotype coding from free text is essential for standardizing health information and improving interoperability between health information systems. Currently, this task is often done ma...

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Detalles Bibliográficos
Autor: Díaz Río, Malena
Tipo de recurso: tesis de maestría
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2025
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/152965
Acceso en línea:https://hdl.handle.net/10609/152965
Access Level:acceso abierto
Palabra clave:Human Phenotype Ontology (HPO), Artificial Intelligence (AI), phenotyping, medical coding, Large Language Model (LLM), Retrieval Augmented Generation (RAG)
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repository_id_str
spelling Automated Phenotype Annotation in Spanish Clinical Text: A Large Language Model Approach with Retrieval-Augmented GenerationDíaz Río, MalenaHuman Phenotype Ontology (HPO), Artificial Intelligence (AI), phenotyping, medical coding, Large Language Model (LLM), Retrieval Augmented Generation (RAG)This work addresses the challenge of automatically annotating phenotypes from unstructured Spanish clinical text. Phenotype coding from free text is essential for standardizing health information and improving interoperability between health information systems. Currently, this task is often done manually by geneticists, increasing workload and delaying patient care. The objective of this work is to optimize the current annotation process by developping an Articial Intelligencebased platform that automates phenotype extraction and mapping. The system analyzes Spanish clinical notes, identifies relevant phenotypes, and links them to corresponding codes from the Human Phenotype Ontology (HPO). The proposed approach combines Large Language Model (LLM) with Retrieval Augmented Generation (RAG), as this method has shown high accuracy in similar tasks in English. Additional benefits include adaptability to ontology updates and easy scalability to other codification systems. The methodology followed in this work involves translating the HPO into Spanish, building a reference vector database, testing prompt strategies, and creating a user interface for the validation and annotation of clinical notes. The entire solution is built on an open-source, microservice-based architecture. In addition, the results of different RAG and LLMs configurations are compared, revealing their effect on accuracy and computational cost. Results show that the LLMs alone (i.e. OpenAI’s o4-mini) performs poorly (F1-score of 0.2), often generating incorrect or unrelated codes. Integrating RAG significantly improves results. The highest-performing configuration achieved reaches an F1-score of 0.64 (0.68 precision and 0.62 recall) on the RAGHPO dataset.Universitat Oberta de Catalunya (UOC)Pérez Millan, AgnèsMadrid García, Alfredo202520252025info:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/submittedVersionapplication/pdfhttps://hdl.handle.net/10609/152965reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésAttribution-NonCommercial 4.0 Internationalhttps://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1529652026-05-28T12:42:01Z
dc.title.none.fl_str_mv Automated Phenotype Annotation in Spanish Clinical Text: A Large Language Model Approach with Retrieval-Augmented Generation
title Automated Phenotype Annotation in Spanish Clinical Text: A Large Language Model Approach with Retrieval-Augmented Generation
spellingShingle Automated Phenotype Annotation in Spanish Clinical Text: A Large Language Model Approach with Retrieval-Augmented Generation
Díaz Río, Malena
Human Phenotype Ontology (HPO), Artificial Intelligence (AI), phenotyping, medical coding, Large Language Model (LLM), Retrieval Augmented Generation (RAG)
title_short Automated Phenotype Annotation in Spanish Clinical Text: A Large Language Model Approach with Retrieval-Augmented Generation
title_full Automated Phenotype Annotation in Spanish Clinical Text: A Large Language Model Approach with Retrieval-Augmented Generation
title_fullStr Automated Phenotype Annotation in Spanish Clinical Text: A Large Language Model Approach with Retrieval-Augmented Generation
title_full_unstemmed Automated Phenotype Annotation in Spanish Clinical Text: A Large Language Model Approach with Retrieval-Augmented Generation
title_sort Automated Phenotype Annotation in Spanish Clinical Text: A Large Language Model Approach with Retrieval-Augmented Generation
dc.creator.none.fl_str_mv Díaz Río, Malena
author Díaz Río, Malena
author_facet Díaz Río, Malena
author_role author
dc.contributor.none.fl_str_mv Pérez Millan, Agnès
Madrid García, Alfredo
dc.subject.none.fl_str_mv Human Phenotype Ontology (HPO), Artificial Intelligence (AI), phenotyping, medical coding, Large Language Model (LLM), Retrieval Augmented Generation (RAG)
topic Human Phenotype Ontology (HPO), Artificial Intelligence (AI), phenotyping, medical coding, Large Language Model (LLM), Retrieval Augmented Generation (RAG)
description This work addresses the challenge of automatically annotating phenotypes from unstructured Spanish clinical text. Phenotype coding from free text is essential for standardizing health information and improving interoperability between health information systems. Currently, this task is often done manually by geneticists, increasing workload and delaying patient care. The objective of this work is to optimize the current annotation process by developping an Articial Intelligencebased platform that automates phenotype extraction and mapping. The system analyzes Spanish clinical notes, identifies relevant phenotypes, and links them to corresponding codes from the Human Phenotype Ontology (HPO). The proposed approach combines Large Language Model (LLM) with Retrieval Augmented Generation (RAG), as this method has shown high accuracy in similar tasks in English. Additional benefits include adaptability to ontology updates and easy scalability to other codification systems. The methodology followed in this work involves translating the HPO into Spanish, building a reference vector database, testing prompt strategies, and creating a user interface for the validation and annotation of clinical notes. The entire solution is built on an open-source, microservice-based architecture. In addition, the results of different RAG and LLMs configurations are compared, revealing their effect on accuracy and computational cost. Results show that the LLMs alone (i.e. OpenAI’s o4-mini) performs poorly (F1-score of 0.2), often generating incorrect or unrelated codes. Integrating RAG significantly improves results. The highest-performing configuration achieved reaches an F1-score of 0.64 (0.68 precision and 0.62 recall) on the RAGHPO dataset.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
info:eu-repo/semantics/submittedVersion
format masterThesis
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10609/152965
url https://hdl.handle.net/10609/152965
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial 4.0 International
https://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial 4.0 International
https://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Oberta de Catalunya (UOC)
publisher.none.fl_str_mv Universitat Oberta de Catalunya (UOC)
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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