# Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages
## 1 Introduction  
With the growing emphasis on patient-centered care, patient-reported outcomes (PROMs) have emerged as a critical tool for understanding and addressing patients' health needs, especially in the management of chronic and complex conditions [1, 2]. PROMs allow the systematic collection of patients' health experiences, symptoms, and quality of life, often using structured instruments such as questionnaires [3, 4]. While effective in many cases, traditional PROMs face notable limitations. These include their reliance on structured, predefined questions that may not capture patients' experiences fully, a lack of adaptability for underrepresented and resource-poor languages, and the significant burden they can place on patients to complete standardized forms [5, 6]. Moreover, current PROMs often fail to accommodate natural language expression, which could offer richer and more nuanced insights into patients' symptoms and health concerns [7]. This issue is particularly pronounced in diverse linguistic contexts, where a lack of tools for resource-poor languages limits inclusivity and equity in data collection and analysis [8, 9]. In light of the limitations of traditional PROMs, there is a growing need for approaches that can capture patient experiences in a more natural and inclusive manner, accommodating diverse linguistic contexts and minimizing patient burden [10]. To address these challenges, advancements in Natural Language Processing (NLP), such as Named Entity Recognition (NER), offer promising tools for extracting patient-reported information directly from unstructured text, enabling a richer and more equitable understanding of health concerns across languages. NER is a fundamental NLP task that involves extracting entities from text and classifying them into predefined categories, such as locations, people, or organizations [11]. In the medical domain, clinical NER models have been developed to identify specific information like diseases, drugs, dosages, and frequencies, with recent advancements leveraging deep learning algorithms to enhance their performance [11]. While progress has been made in medical NER for resource-abundant languages, there remains a notable research gap concerning resource-poor languages [12]. Addressing this gap is important to ensure equitable healthcare advancements across various language groups. Moreover, beyond structuring medical notes, there is a need to extract symptoms or problems as expressed by patients in their own words, facilitating a more patient-centered approach to healthcare. This paper describes a weakly-supervised pipeline for training and evaluating medical NER models across eight languages: English, German, Greek, Spanish, Italian, Polish, Portuguese, and Slovenian. We refer to the approach as \"weakly-supervised\" because it relies on automatically annotated data rather than manually labeled gold-standard datasets, using state-of-the-art models to generate training labels. By fine-tuning BERT-based models, we enabled the extraction of medical entities like symptoms, treatments, and tests from medical and everyday texts. The models will be used in a multicentric feasibility study of the SMILE project, as part of digital health interventions to support the (mental) well-being of children across multiple sites in Germany, Italy, the UK, Spain, Cyprus, Poland, and Slovenia. This research focuses on underrepresented languages and patient-expressed symptoms, as it seeks to enhance the accessibility and quality of healthcare information extraction. Clinical Named Entity Recognition (NER) models have made significant progress in structuring and summarizing clinical notes, predominantly in English. However, the existing models face several challenges, including monolingual focus, inconsistent performance reporting, issues with non-clinical semantics in reporting, and limited adaptability to low-resource languages [13; 14; 15].