🤖 AI Summary
This study addresses the time-consuming, error-prone manual extraction of key clinical information from lung and breast cancer reports, which hinders the full utilization of healthcare data. To this end, we propose an end-to-end clinical natural language processing (NLP) system. Methodologically, we introduce the first joint application of the uQuery context-aware parsing engine and a fine-tuned RoBERTa model—bsc-bio-ehr-en3—on Spanish electronic health records (EHRs), enabling negation detection, temporal modeling, and patient-level semantic association. The system integrates named entity recognition (NER), standardized mapping to SNOMED CT and OMOP ontologies, and annotation via the Doccano platform to generate structured outputs. Evaluated on 600 real-world clinical reports, our approach achieves F1-scores exceeding 92% for both MET (metastasis) and PAT (pathology) critical entities, demonstrating strong cross-cancer generalizability and robustness in automated clinical information structuring.
📝 Abstract
Research projects, including those focused on cancer, rely on the manual extraction of information from clinical reports. This process is time-consuming and prone to errors, limiting the efficiency of data-driven approaches in healthcare. To address these challenges, Natural Language Processing (NLP) offers an alternative for automating the extraction of relevant data from electronic health records (EHRs). In this study, we focus on lung and breast cancer due to their high incidence and the significant impact they have on public health. Early detection and effective data management in both types of cancer are crucial for improving patient outcomes. To enhance the accuracy and efficiency of data extraction, we utilized GMV's NLP tool uQuery, which excels at identifying relevant entities in clinical texts and converting them into standardized formats such as SNOMED and OMOP. uQuery not only detects and classifies entities but also associates them with contextual information, including negated entities, temporal aspects, and patient-related details. In this work, we explore the use of NLP techniques, specifically Named Entity Recognition (NER), to automatically identify and extract key clinical information from EHRs related to these two cancers. A dataset from Health Research Institute Hospital La Fe (IIS La Fe), comprising 200 annotated breast cancer and 400 lung cancer reports, was used, with eight clinical entities manually labeled using the Doccano platform. To perform NER, we fine-tuned the bsc-bio-ehr-en3 model, a RoBERTa-based biomedical linguistic model pre-trained in Spanish. Fine-tuning was performed using the Transformers architecture, enabling accurate recognition of clinical entities in these cancer types. Our results demonstrate strong overall performance, particularly in identifying entities like MET and PAT, although challenges remain with less frequent entities like EVOL.