UCL Discovery is UCL's open access repository, showcasing and providing access to UCL research outputs from all UCL disciplines.

Introduction The study objective was to determine the value contribution of palopegteriparatide to the treatment of adults with chronic hypoparathyroidism, a rare endocrine disease, through a multi-cr...

In a groundbreaking study published in BMC Health Services Research, researchers have explored the intricate process of prioritizing access to Intensive Care Unit (ICU) beds in critical care environments. This pressing issue has become increasingly relevant in recent years, exacerbated by global health crises such as the COVID-19 pandemic. The study presents a multicriteria approach designed to rank access to these vital resources, ensuring that healthcare providers can make informed decisions in time-sensitive and life-or-death scenarios.

In Brazil, the quest for improved quality of life through effective water supply is a critical challenge, exacerbated by rapid urbanization, climate change, and socio-economic disparities. To tackle this pressing issue, the pioneering work conducted by researchers O.H.C. Hamdan, M. Libânio, and V.A.F. Costa has introduced the Service Quality Index (SQI), a comprehensive multicriteria approach designed to evaluate water supply systems across the nation.

« Psychosociology theories indicate that individual evaluation is integral to the recognition of professional activities. Building upon Christophe Dejours’ contributions, this recognition is influe…

> a multicriteria assessment of 24 meat and milk alternatives that integrates nutritional, health, environmental, and cost analyses with a focus on high-income countries.

UCL Discovery is UCL's open access repository, showcasing and providing access to UCL research outputs from all UCL disciplines.

This stage aims to establish key evaluation criteria based on the properties of the selected biomass. For this study, the criteria include lignin, cellulose, hemicellulose, and the contents of carbon, hydrogen, nitrogen, sulfur, and oxygen. High levels of lignin, carbon,...

This corpus will be made publicly available to the scientific community. JOURNAL OF MEDICAL INTERNET RESEARCH. Mata et al https://www.jmir.org/2025/1/ ...

Journal of Medical Internet Research · Journal of Medical Internet Research 10086 articles · JMIR Research Protocols 4783 articles · JMIR Formative ...

Background: Heart failure with preserved ejection fraction (HFpEF) is a major clinical manifestation of cardiac amyloidosis (CA), a condition frequently underdiagnosed due to its nonspecific symptomatology. Electronic health records (EHRs) offer a promising avenue for supporting early symptom detection through natural language processing (NLP). However, identifying relevant clinical cues within unstructured narratives, particularly in Spanish, remains a significant challenge owing to the scarcity of annotated corpora and domain-specific models. Objective: This study proposes and evaluates a Transformer-based NLP framework for the automated detection of HFpEF-related symptoms in Spanish EHRs. Our objective is to assess the feasibility of leveraging clinical narratives to support early identification of heart failure phenotypes suggestive of cardiac amyloidosis. Methods: A novel corpus was developed from over 15,000 Spanish clinical documents, manually annotated and validated by cardiology experts. Several Transformer architectures were benchmarked, including general-purpose, biomedical-specialized, and long-document models (Longformers), under three optimization strategies tailored to clinically relevant metrics: area under the curve (AUC), F1-score, and sensitivity. Results: All models achieved strong performance (AUC > 0.940). The best-performing model, Longformer Biomedical-Clinical, reached an AUC of 0.987 and F1-score of 0.985. Sensitivity-optimized models achieved false negative rates below 3%, a critical threshold for clinical applicability. Conclusions: Transformer-based models can robustly detect HFpEF-related symptoms from unstructured Spanish clinical texts, even in the presence of class imbalance and complex language patterns. Our findings highlight the importance of combining domain-specific pretraining, long-context modeling, and tailored optimization to enhance the performance of NLP systems in high-impact clinical applications.

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