# Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages
## 4 Discussion  
The presented weakly-supervised multilingual NER pipeline addresses the key limitations identified in the existing approaches, including single-language focus [20, 25], reliance on large, annotated datasets, the inability to handle informal patient language [5, 6, 7], and limited adaptation to resource-poor languages [12, 13, 14]. Namely, traditional medical NER models like Stanza [17] and GERNERMED [19] demonstrate effectiveness in single languages but struggle with multilingual adaptability [30]. Moreover, the existing multilingual models often depend on extensive annotated datasets, making them impractical for resource-poor languages [12, 13]. The proposed approach overcomes these constraints through weak supervision and efficient language-language translation pipelines, enabling robust performance across multiple languages, including underrepresented ones such as Slovenian and Polish. A critical challenge in current medical NER models is their limited adaptability across different types of language [10, 15]. Traditional medical NER systems, trained primarily on clinical documentation, often struggle to recognize symptoms when patients use colloquial expressions or metaphorical language to describe their conditions [5, 8]. Namely, while models like Stanza [17] and MedPsyNIT [20] achieve high performance on formal clinical notes (F1 scores of 88.1% and 89.5% respectively), their effectiveness diminishes significantly when processing informal patient descriptions. This limitation is particularly evident when trying to exploit the medical NER concept in the context of patient-reported outcomes, where individuals describe their symptoms in natural, conversational language rather than clinical terminology [6, 7]. For instance, while a clinician might document \"pyrexia\", patients typically describe \"feeling hot\" or \"burning up\". We demonstrate this with a series of case studies, comparing existing medical NERs in English, Italian, and Spanish, where our model overcame the gap between clinical precision and patient expression significantly, making it more suitable for processing real-world patient-reported health data across languages. Moreover, the increased performance over the existing models, especially on the non-medical terminology (English: 67.69% to 80.07%, Italian: 57.06% to 75.60%, Spanish: 62.60% to 77.61%), demonstrates the effectiveness of our approach further. The results show clearly that our models (and the pipeline) can complement the traditional PROMs efficiently by extracting symptoms from natural language descriptions, supporting more patient-centered data collection. This aligns well with recent research highlighting the importance of natural language processing in healthcare [10], while extending its applicability to multiple languages. The current hyperparameter configuration, while effective, still has room for optimization through more extensive tuning. Secondly, the quality of the machine translation affects model performance, particularly for low-resource languages with less reliable translation tools. This limitation connects to broader challenges in multilingual NLP noted by [PERSON] et al. [14]. Translation errors, particularly in the case of ambiguous or domain-specific terms, can lead to misinterpretations that may degrade model performance. These challenges are especially evident when handling medical terminology, where incorrect translation may result in inaccurate annotations and entity recognition. Addressing these errors through improved translation models or post-translation correction could mitigate their impact. Thirdly, while our automated annotation process using Stanza [17] enabled efficient dataset creation, it may propagate biases from the base model. A fully manual annotation process would provide more reliable training data but require prohibitive time and cost investments.