Comparando enfoques deep learning en una fase y en dos fases para extraer interacciones farmacológicas de texto

Drug-drug interactions (DDI) are a cause of adverse drug reactions. They occur when a drug has an impact on the effect of another drug. There is not a complete, up to date database where health care professionals can consult the interactions of any drug because most of the knowledge on DDI is hidden in unstructured text. In last years, deep learning has been succesfully applied to the extraction of DDI from texts, which requires the detection and later classification of DDI. Most of the deep learning systems for DDI extraction developed so far have addressed the detection and classification in one single step. In this study, we compare the performance of one-stage and two-stage architectures for DDI extraction. Our architectures are based on a bidirectional recurrent neural network layer composed of Gated Recurrent Units. The two-stage system obtained a 67.45 % micro-average F1 score on the test set.Las interacciones farmacológicas (DDI) son una de las causas de reacciones adversas a medicamentos. Ocurren cuando una medicina interfiere en la acción de una segunda. En la actualidad, no existe una base de datos completa y actualizada donde los profesionales de la salud puedan consultar las interacciones de cualquier medicamento porque la mayor parte del conocimiento sobre DDIs está oculto en texto no estructurado. En los últimos años, el aprendizaje profundo se ha aplicado con éxito a la extracción de DDIs de los textos, lo que requiere la detección y posterior clasificación de DDIs. La mayoría de los sistemas de aprendizaje profundo para extracción de DDIs desarrollados hasta ahora han abordado la detección y clasificación en un solo paso. En este estudio, comparamos el rendimiento de las arquitecturas de una y dos etapas para la extracción de DDI. Nuestras arquitecturas se basan en una capa de red neuronal recurrente bidireccional compuesta de Gated Recurrent Units (GRU). El sistema en dos etapas obtuvo un puntaje F1 promedio de 67.45 % en el dataset de evaluación.This work was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R)

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

PLN aplicado a salud laboral: tarea MEDDOPROF en IberLEF 2021 sobre detección, clasificación y normalización automática de profesiones y ocupaciones en textos médicos

Entre las características sociodemográficas de los pacientes, las ocupaciones juegan un papel fundamental tanto desde el punto de vista de la salud laboral, accidentes laborales y exposición a tóxicos y patógenos como desde el de la salud física y mental. Este artículo presenta la tarea Medical Documents Profession Recognition (MEDDOPROF), celebrada dentro de IberLEF/SEPLN 2021. La tarea se centra en el reconocimiento y detección de ocupaciones en textos médicos en castellano. MEDDOPROF propone tres retos: NER (reconocimiento de profesiones, situaciones laborales y actividades), CLASS (clasificar cada ocupación en función de su referente, como puede ser el paciente o un familiar) y NORM (normalizar menciones a las terminologías ESCO y SNOMED-CT). De un total de 40 equipos registrados, 15 han presentado un total de 94 sistemas. Los sistemas de mejor rendimiento se basan en tecnologías de aprendizaje profundo como transformers, llegando a conseguir una F-score de 0.818 en detección de ocupaciones (NER), 0.793 en clasificación de ocupaciones por su referente (CLASS) y 0.619 en normalización (NORM). Futuras iniciativas deberían tener también en cuenta aspectos multilingües y la aplicación en otros dominios como servicios sociales, recursos humanos, análisis del mercado legal y laboral o la política.Among the socio-demographic patient characteristics, occupations play an important role regarding not only occupational health, work-related accidents and exposure to toxic/pathogenic agents, but also their impact on general physical and mental health. This paper presents the Medical Documents Profession Recognition (MEDDOPROF) shared task (held within IberLEF/SEPLN 2021), focused on the recognition and normalization of occupations in medical documents in Spanish. MEDDOPROF proposes three challenges: NER (recognition of professions, employment statuses and activities in text), CLASS (classifying each occupation mention to its holder, i.e. patient or family member) and NORM (normalizing mentions to their identifier in ESCO or SNOMED CT). From the total of 40 registered teams, 15 submitted a total of 94 runs for the various sub-tracks. Best-performing systems were based on deep-learning technologies (incl. transformers) and achieved 0.818 F-score in occupation detection (NER), 0.793 in classifying occupations to their referent (CLASS) and 0.619 in normalization (NORM). Future initiatives should also address multilingual aspects and application to other domains like social services, human resources, legal or job market data analytics and policy makers.MEDDOPROF was promoted through the collaboration between the Spanish Plan for the Advancement of Language Technology (Plan TL) and the BSC. We also want to acknowledge the 2020 Proyectos de I+D+I - RTI Tipo A (DESCIFRANDO EL PAPEL DE LAS PROFESIONES EN LA SALUD DE LOS PACIENTES A TRAVES DE LA MINERIA DE TEXTOS (PID2020-119266RA-I00)) for support

PharmaCoNER corpus: gold standard annotations of Pharmacological Substances, Compounds and proteins in Spanish clinical case reports

Intro: PharmaCoNER shared task dataset (divided into train, dev and test). In addition, we include here the PharmaCoNER background set. It contains the train, development and test sets of the two subtasks (subtask-1 and subtask-2) with Gold Standard annotations. In addition, it contains the documents of the background set, without annotations. Please, cite: A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10. Annotation quality Inter-annotator agreement: 93% for annotation, 73% for mapping. For more information, see the paper. Format For subtask 1 annotations are distributed in Brat format. (More info at Brat webpage https://brat.nlplab.org/standoff.html) For subtask-2, codes are associated with each document are given in a TSV file with the following columns: filename code Shared task goal: In the two subtasks, the goal is to predict the annotations of the test files (either the ANN files or the TSV with the codes) given only the plain text files. Resources: Web Citation: A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10. Silver Standard corpus Annotation guidelines PharmaCoNER tagger For further information, please visit https://temu.bsc.es/pharmaconer/ or email us at [email protected] Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL)

LivingNER corpus: Named entity recognition, normalization & classification of species, pathogens and food

LivingNER Gold Standard corpus (includes training, validation, test and background sets + MULTILINGUAL RESOURCES) ** NEW February 28th 2023: Annotated Test Set Public Release ** Please cite if you use this dataset: A. Miranda-Escalada, E. Farré-Maduell, S. Lima-López, D. Estrada, L. Gascó, M. Krallinger, Mention detection, normalization & classification of species, pathogens, humans and food in clinical documents: Overview of LivingNER shared task and resources, Procesamiento del Lenguaje Natural (2022) @article{amiranda2022nlp, title={Mention detection, normalization \& classification of species, pathogens, humans and food in clinical documents: Overview of LivingNER shared task and resources}, author={Miranda-Escalada, Antonio and Farr{\'e}-Maduell, Eul{`a}lia and Lima-L{\'o}pez, Salvador and Estrada, Darryl and Gasc{\'o}, Luis and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, year={2022} } 1. Introduction The LivingNER Gold Standard corpus is a collection of 2000 clinical cases from over 10 different medical areas annotated with SPECIES and HUMAN mentions, that are mapped to NCBI Taxonomy. It was used for the LivingNER Shared Task on pathogens and living beings detection and normalization in Spanish medical documents, which was celebrated as part of IberLEF 2022. 2. Training, validation, test and background sets The training set is composed of 1000 clinical case reports extracted from miscellaneous medical specialties including COVID, oncology, infectious diseases, tropical medicine, urology, pediatrics, and others. The validation set includes 500 clinical case reports with the same characteristics and the test set includes 485. The background set is a collection of around 13k unannotated case reports that were originally added to prevent manual annotations in the test set during the competition and to create a Silver Standard. 2.1 Annotations format Annotations and text files are distributed separately. The texts are in plain text (.txt in UTF-8) format, while the annotations are are distributed in a tab-separated file (.tsv) file with one row per annotation: - For subtask 1 (LivingNER-Species NER track), the .tsv file has the following columns: filename: document name mark: identifier mention mark label: mention type (SPECIES or HUMAN) off0: starting position of the mention in the document off1: ending position of the mention in the document span: textual span - For subtask 2 (LivingNER-Species Norm track), the .tsv file has the same columns as the previous one, plus: isH: whether the span is narrower than the NCBITax assigned code isN: whether the mention corresponds to a nosocomial infection iscomplex: whether the span has assigned a combination of NCBITax codes NCBITax: mention code in the NCBI Taxonomy - For subtask 3 (LivingNER-Clinical IMPACT track), the .tsv file has the following columns: filename isPet (Yes/No) PetIDs (NCBITaxonomy codes of pet & farm animals present in document) isAnimalInjury (Yes/No) AnimalInjuryIDs (NCBITaxonomy codes of animals causing injuries present in document) IsFood (Yes/No) FoodIDs (NCBITaxonomy codes of food mentions present in document) isNosocomial (Yes/No) NosocomialIDs (NCBITaxonomy codes of nosocomial species mentions present in document) 2.2 Important notes about subtask 3 (LivingNER-Clinical IMPACT track): Less clinical case reports. Subtask 3 (LivingNER-Clinical IMPACT track) contains half of the clinical case reports (500 in the training partition, 250 in the validation partition). The list of valid clinical case reports for task 3 is included in the data (train_files_task3.txt and validation_files_task3.txt) Enriched dataset. The GS format is the one described above (a TSV with one line per clinical case report). However, we believe participants may find useful and enriched dataset. Then, we provide an additional dataset, with the mentions of the NER track classified in the 4 Clinical impact categories (food, pet&farm animals, animals causing injuries and nosocomial). It is a TSV file with one row per annotation, and with the following columns: filename, mark, label, off0, off1, span, isPet, isAnimalInjury, isFood, isNosocomial, isH, iscomplex, code 3. Multilingual resources We have generated the annotated training and validation sets in 7 languages: English, Portuguese, Catalan, Galician, Italian, French and Romanian. The process was: The text files were translated with a neural machine translation system. The annotations were translated with the same neural machine translation system. The translated annotations were transferred to the translated text files using an annotation transfer technology. The text files are stored in the multilingual_resources/training-text-files and multilingual_resources/validation-text-files subfolders. The annotated TSV files are stored in the multilingual_resources/annotation_transfer subfolder. For the sake of comparison, we incorporate as well the annotations that resulted from the LINNAEUS tool in the multilingual_resources/linneaus subfolder. If you want to visualize the multilingual resources, check out this Brat server: https://temu.bsc.es/mLivingNER/#/translations/ For instance, you can see the parallel annotations in English vs in French, or in Spanish (the gold standard) vs in Catalan. Related resources Web Annotation guidelines Evaluation library LivingNER terminology For further information, please visit https://temu.bsc.es/livingner/ or email us at [email protected] by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL)

Detección, normalización y clasificación de especies, patógenos, humanos y alimentos en documentos clínicos: resumen de la tarea y los recursos LivingNER

There is a pressing need to generate tools for finding mentions of species, pathogens, or food from medical texts. To promote the development of such tools we organized the LivingNER task. LivingNER relied on a large Gold Standard corpus of 2000 carefully selected clinical cases in Spanish covering diverse specialties. It was manually annotated with species mentions that were also carefully mapped to their corresponding NCBI Taxonomy identifiers. Besides, we have generated Silver Standard versions of LivingNER for 7 languages: English, Portuguese, Galician, Catalan, Italian, French, and Romanian. LivingNER had three subtasks: LivingNERSpecies NER (species mention detection sub-task), LivingNER-Species Norm (species mention detection and normalization to NCBI taxonomy Ids), and LivingNERClinical IMPACT (a document classification task related to the detection of pets, animals-causing injuries, food, and nosocomial entities). We received and evaluated 62 systems from 20 teams from 11 countries worldwide, obtaining highly competitive results. Successful approaches typically modified pre-trained transformer-like language models (BERT, BETO, RoBERTa, etc.) and employed embedding distance metrics for entity linking. LivingNER corpus: doi.org/10.5281/zenodo.6376662Existe la necesidad de generar herramientas para encontrar y normalizar menciones de especies, patógenos o alimentos en textos médicos. Para promover el desarrollo de tales herramientas hemos organizado la tarea LivingNER. La tarea LivingNER se basó en un corpus en español de 2000 casos clínicos cuidadosamente seleccionados, representando una diversidad de especialidades. El corpus fue anotado manualmente por expertos que también asignaron a las menciones sus correspondientes identificadores de la NCBI Taxonomy. Además, hemos generado versiones de LivingNER para otros 7 idiomas: inglés, portugués, gallego, catalán, italiano, francés y rumano. LivingNER se estructuró en tres subtareas: 1) LivingNER-Species NER (subtarea de detección de menciones de especies), 2) LivingNER-Species Norm (detección de especies y normalización a identificadores de NCBI Taxonomy) y 3) LivingNER-Clinical IMPACT (tarea de clasificación relacionada con la detección de mascotas, animales causantes de lesiones, alimentos y entidades nosocomiales). Recibimos y evaluamos 62 sistemas de 20 equipos de 11 países a nivel mundial, obteniendo resultados altamente competitivos. Generalmente, los enfoques más exitosos hicieron modificaciones a modelos de lenguaje basados en transformers (BERT, BETO, RoBERTa, etc.) y emplearon métricas de distancia de embeddings para la normalización de entidades. Corpus LivingNER: doi.org/10.5281/zenodo.6376662This project is supported by the European Union’s Horizon Europe Coordination & Support Action under Grant Agreement No 101058779. We acknowledge the support from the AI4PROFHEALTH project (PID2020-119266RA-I00)

Executive summary on the treatment of type 2 diabetes mellitus in elderly or frail individuals. 2022 update of the 2018 consensus document "Treatment of type 2 diabetes mellitus in the elderly" Resumen ejecutivo sobre el tratamiento de la diabetes mellitus tipo 2 en personas de edad avanzada o frágiles. Actualización 2022 del documento de consenso 2018 «Tratamiento de la diabetes mellitus tipo 2 en el paciente anciano»

The population with type 2 DM (DM2) is highly heterogeneous, representing an important challenge for healthcare professionals. The therapeutic choice should be individualized, considering the functional status, frailty, the occurrence of comorbidities, and the preferences of patients and their caregivers. New evidence on the cardiovascular and renal protection of specific therapeutic groups and on the usefulness of new technologies for DM2 management, among other aspects, warrant an update of the consensus document on the DM2 in the elderly that was published in 2018

Organic photocatalysts for the oxidation of pollutants and model compounds

The use of organic photocatalysts for the oxidation of pollutants and model compounds, with special emphasis on the involved concepts and mechanistic aspects, is reviewed. Photoinduced electron transfer involving the singlet excited state of phthalocyanines has been postulated in the photodegradation of phenols. The reactivity of OH - radical with different pollutants has been quantitatively determined by competition experiments, looking at the decrease of the typical transient absorption of the stilbene adduct at 390 nm. As regards heterogeneous media, after excitation at 355 nm of phthalocyanines immobilized in zeolites, the characteristic singlet oxygen luminiscence is monitored at 1270 nm. TPP has been included within extralarge pore zeolitic aluminosilicates, such as MCM-41. These materials provide an adequate balance between moderate cage effect and facilitation of molecular traffic through the mesopores.Financial support from the Spanish Government (CTQ2009- 13699, CTQ2009-13459-C05-03/PPQ, RIRRAF RETICS), and the Generalitat Valenciana (Prometeo Program) is gratefully acknowledged. Dedicated to Prof. Avelino Corma on the occasion of his 60th birthday.Marín García, ML.; Santos-Juanes Jordá, L.; Arqués Sanz, A.; Amat Payá, AM.; Miranda Alonso, MÁ. (2012). Organic photocatalysts for the oxidation of pollutants and model compounds. Chemical Reviews. 112(3):1710-1750. doi:10.1021/cr2000543S17101750112