20.500.12556/DKUM-92857 Weakly-supervised multilingual medical NER for symptom extraction for low-resource languages Patient-reported health data, especially patient-reported outcomes measures, are vital for improving clinical care but are often limited by memory bias, cognitive load, and inflexible questionnaires. Patients prefer conversational symptom reporting, highlighting the need for robust methods in symptom extraction and conversational intelligence. This study presents a weakly-supervised pipeline for training and evaluating medical Named Entity Recognition (NER) models across eight languages, with a focus on low-resource settings. A merged English medical corpus, annotated using the Stanza i2b2 model, was translated into German, Greek, Spanish, Italian, Portuguese, Polish, and Slovenian, preserving the entity annotations medical problems, diagnostic tests, and treatments. Data augmentation addressed the class imbalance, and the fine-tuned BERT-based models outperformed baselines consistently. The English model achieved the highest F1 score (80.07%), followed by German (78.70%), Spanish (77.61%), Portuguese (77.21%), Slovenian (75.72%), Italian (75.60%), Polish (75.56%), and Greek (69.10%). Compared to the existing baselines, our models demonstrated notable performance gains, particularly in English, Spanish, and Italian. This research underscores the feasibility and effectiveness of weakly-supervised multilingual approaches for medical entity extraction, contributing to improved information access in clinical narratives—especially in under-resourced languages. low-resource languages machine translation medical entity extraction NER NLP patient-reported outcomes weakly-supervised learning strojno prevajanje medicinska entiteta rezultati poročil bolnikov slabo nadzorovano učenje true true false MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/2076-3417/15/10/5585 © 2025 by the authors Članek v reviji 2025-05-19 16:04:39 2025-05-19 16:04:39 2025-05-20 03:15:23 0000-00-00 00:00:00 2025 0 0 18 str. iss. 10, [article no.] 5585 Vol. 15 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-05-01 2025-05-13 2025-05-16 004.8:61 236281347 10.3390/app15105585 2076-3417 applsci-15-05585-v2.pdf applsci-15-05585-v2.pdf 1 9E3606C205F09FCCA4B26DDF5C379DCF b92d1b66db513c399ae8d97a6a503fef774a62c3160fd70220cf24d383ba3ab8 e4cdecdc-34ba-11f0-ad3e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=191116 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0