15 research outputs found

    Localising In-Domain Adaptation of Transformer-Based Biomedical Language Models

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    In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with task-specific, fine-tuned biomedical language representation models, improving patient care and management. For such specialized domains, previous research has shown that fine-tuning models stemming from broad-coverage checkpoints can largely benefit additional training rounds over large-scale in-domain resources. However, these resources are often unreachable for less-resourced languages like Italian, preventing local medical institutions to employ in-domain adaptation. In order to reduce this gap, our work investigates two accessible approaches to derive biomedical language models in languages other than English, taking Italian as a concrete use-case: one based on neural machine translation of English resources, favoring quantity over quality; the other based on a high-grade, narrow-scoped corpus natively written in Italian, thus preferring quality over quantity. Our study shows that data quantity is a harder constraint than data quality for biomedical adaptation, but the concatenation of high-quality data can improve model performance even when dealing with relatively size-limited corpora. The models published from our investigations have the potential to unlock important research opportunities for Italian hospitals and academia. Finally, the set of lessons learned from the study constitutes valuable insights towards a solution to build biomedical language models that are generalizable to other less-resourced languages and different domain settings.Comment: 8 pages, 2 figures, 6 tables. Published in Journal of Biomedical Informatic

    Etiological treatment in psychiatry: the neurosyphilis model

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    This paper aims to study the historical evolution and describe the impact of modern antibiotic therapy on psychiatric hospital admissions. The data was collected in the hospital data bank with records of patients admissions from 1931 to 1991. Patients were classified by name, sex, age, marital status, social class, nationality, place of birth, occupation, number of admissions by neurosyphilis and other diagnosis as also date of admission and state of health at time of leaving hospital, and this information was used in the statistical analysis. The classification system of diagnosis is that used by WHO ICD - 9. The results show decreasing rates of admissions by neurosyphilis after the introduction of penicillin in 1948 (19%) to the last admission in the historical cohort in 1968. The antibiotics (penicillin) change the natural evolution of the disease and its pattern of morbidity and mortality. The therapeutical impact of antibiotics in the incidence and prevalence of hospitalization rates of neurosyphilis is never observed in any other psychiatric disease.Este trabalho tem como objetivo estudar a evolução histórica, durante 60 anos, das admissões por diagnóstico de neurossífilis em um hospital psiquiátrico e descrever o impacto da moderna antibioticoterapia. Foi baseado nos dados e planilhas do hospital psiquiátrico em estudo, de 1931 à 1991. O banco de dados do hospital contém informações do tipo: nome, sexo, idade, estado civil, nacionalidade, procedência, profissão, classe social, número de admissões por neurossífilis e outros diagnósticos, data de admissão, data de alta e diagnóstico pelo CID 9/WHO. A análise estatística envolveu todas as admissões até o último registro de paciente com diagnóstico de neurossífilis e comparado com resultados por outros tipos de admissões. Observou-se que antes da introdução da penicilina, nos serviços de saúde de Pelotas, em 1948, 19% das admissões eram por neurossífilis. Depois, os registros hospitalares descrevem uma tendência decrescente até a última admissão em 1968. O impacto da antibioticoterapia na história natural da neurossífilis foi o maior até hoje e nunca antes visto com outros recursos terapêuticos (biológicos, psicofarmacológicos e psicoterápicos), para outras doenças psiquiátricas.UCPel.Universidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Departamento de PsiquiatriaUNIFESP, EPM, Depto. de PsiquiatriaSciEL

    Advancing Italian Biomedical Information Extraction with Large Language Models: Methodological Insights and Multicenter Practical Application

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    The introduction of computerized medical records in hospitals has reduced burdensome operations like manual writing and information fetching. However, the data contained in medical records are still far underutilized, primarily because extracting them from unstructured textual medical records takes time and effort. Information Extraction, a subfield of Natural Language Processing, can help clinical practitioners overcome this limitation, using automated text-mining pipelines. In this work, we created the first Italian neuropsychiatric Named Entity Recognition dataset, PsyNIT, and used it to develop a Large Language Model for this task. Moreover, we conducted several experiments with three external independent datasets to implement an effective multicenter model, with overall F1-score 84.77%, Precision 83.16%, Recall 86.44%. The lessons learned are: (i) the crucial role of a consistent annotation process and (ii) a fine-tuning strategy that combines classical methods with a "few-shot" approach. This allowed us to establish methodological guidelines that pave the way for future implementations in this field and allow Italian hospitals to tap into important research opportunities

    Antimicrobiano parenteral exclusivo ou associado com a via oral na prevenção de infecção após cirurgia colorretal

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    Estudo prospectivo da incidência de complicações infecciosas, após operações para câncer colorretal. Sessenta e nove pacientes foram divididos em dois grupos, tendo o primeiro grupo recebido neomicina e metronidazol, por via oral, associados a gentamicina e metronizadol por via parenteral e o segundo grupo somente gentamicina e metronidazol por via parenteral. Foi estabelecido como objetivo principal a avaliação da influência do antimicrobiano administrado no preparo intestinal sobre a incidência de complicações infecciosas pós-operatórias. Os pacientes que receberam antimicrobianos por via oral no preparo intestinal apresentaram menor porcentagem de complicações infecciosas (14,29%) em relação aos pacientes que receberam apenas antimicrobiano por via parenteral (38,24%), sendo esta diferença estatisticamente significante, em nível de 5%. Esses dados apóiam a sugestão de associar antimicrobianos por via oral aos antimicrobianos por via parenteral na tentativa de reduzir as complicações infecciosas na cirurgia colorretal

    Italian, European, And International Neuroinformatics Efforts: An Overview

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    Neuroinformatics is a research field that focuses on software tools capable of identifying, analysing, modelling, organising and sharing multi-scale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big-Data phenomenon, characterised by the so-called 3Vs (Volume, Velocity, and Variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic, and radiological technologies. This situation has led to a "data deluge", as neuroscientists can routinely collect more study-data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focused neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap, and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS, GAAIN, BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI, CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience worldwide

    Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

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    : Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

    Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

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    Abstract Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis
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