Chronic liver disease-associated severe thrombocytopenia in Spain: Results from a retrospective study using machine learning and natural language processing / Trombocitopenia grave asociada a enfermedad hepática crónica en España: Resultados de un estudio retrospectivo utilizando aprendizaje automático y procesamiento de lenguaje natural

Background Patients with chronic liver disease (CLD) often develop thrombocytopenia (TCP) as a complication. Severe TCP (platelet count<50×109/L) can increase morbidity and complicate CLD management, increasing bleeding risk during invasive procedures. Objectives To describe the real-world scenario of CLD-associated severe TCP patients’ clinical characteristics. To evaluate the association between invasive procedures, prophylactic treatments, and bleeding events in this group of patients. To describe their need of medical resource use in Spain. Methods This is a retrospective, multicenter study including patients who had confirmed diagnosis of CLD and severe TCP in four hospitals within the Spanish National Healthcare Network from January 2014 to December 2018. We analyzed the free-text information from Electronic Health Records (EHRs) of patients using Natural Language Processing (NLP), machine learning techniques, and SNOMED-CT terminology. Demographics, comorbidities, analytical parameters and characteristics of CLD were extracted at baseline and need for invasive procedures, prophylactic treatments, bleeding events and medical resources used in the follow up period. Frequency tables were generated for categorical variables, whereas continuous variables were described in summary tables as mean (SD) and median (Q1–Q3). Results Out of 1,765,675 patients, 1787 had CLD and severe TCP; 65.2% were male with a mean age of 54.7 years old. Cirrhosis was detected in 46% (n=820) of patients and 9.1% (n=163) had hepatocellular carcinoma. Invasive procedures were needed in 85.6% of patients during the follow up period. Patients undergoing procedures compared to those patients without invasive procedures presented higher rates of bleeding events (33% vs 8%, p<0.0001) and higher number of bleedings. While prophylactic platelet transfusions were given to 25.6% of patients undergoing procedures, TPO receptor agonist use was only detected in 3.1% of them... (AU)

Antecedentes Los pacientes con enfermedad hepática crónica (EHC) a menudo desarrollan trombocitopenia (TCP) como agravante de su enfermedad. La TCP grave (definida por un recuento de plaquetas < 50 x 109/L) puede aumentar la morbilidad y complicar el manejo de la EPC, incrementando el riesgo de hemorragia durante los procedimientos invasivos. Objetivos Describir el escenario de mundo real de las características clínicas de los pacientes con TCP grave asociado a EHC. Evaluar la asociación entre procedimientos invasivos, tratamientos profilácticos y eventos hemorrágicos en este grupo de pacientes, así como describir el uso de recursos médicos en España. Métodos Se plantea un estudio multicéntrico retrospectivo que incluye pacientes con diagnóstico confirmado de EHC y TCP grave en cuatro hospitales de la Red Nacional de Salud de España desde enero de 2014 hasta diciembre de 2018. Analizamos la información de texto libre de la Historia Clínica Electrónica (HCE) de pacientes que utilizan procesamiento de lenguaje natural (PLN), técnicas de aprendizaje automático y terminología de SNOMED-CT. Los datos demográficos, las comorbilidades, los parámetros analíticos y las características de la EHC se extrajeron al inicio del estudio, así como la necesidad de procedimientos invasivos, tratamientos profilácticos, eventos hemorrágicos y recursos médicos utilizados en el periodo de seguimiento. Se generaron tablas de frecuencia para las variables categóricas, mientras que las variables continuas se describieron en tablas resumen como media (SD) y mediana (Q1-Q3). Resultados De 1.765.675 pacientes identificados, 1.787 tenían EHC y TCP grave, siendo el 65,2% varones con una edad media de 54,7 años. Se detectó cirrosis en el 46% (n = 820) de los pacientes y el 9,1% (n = 163) de ellos presentaron un diagnóstico de carcinoma hepatocelular... (AU)

Chronic liver disease-associated severe thrombocytopenia in Spain: Results from a retrospective study using machine learning and natural language processing.

BACKGROUND: Patients with chronic liver disease (CLD) often develop thrombocytopenia (TCP) as a complication. Severe TCP (platelet count<50×109/L) can increase morbidity and complicate CLD management, increasing bleeding risk during invasive procedures. OBJECTIVES: To describe the real-world scenario of CLD-associated severe TCP patients' clinical characteristics. To evaluate the association between invasive procedures, prophylactic treatments, and bleeding events in this group of patients. To describe their need of medical resource use in Spain. METHODS: This is a retrospective, multicenter study including patients who had confirmed diagnosis of CLD and severe TCP in four hospitals within the Spanish National Healthcare Network from January 2014 to December 2018. We analyzed the free-text information from Electronic Health Records (EHRs) of patients using Natural Language Processing (NLP), machine learning techniques, and SNOMED-CT terminology. Demographics, comorbidities, analytical parameters and characteristics of CLD were extracted at baseline and need for invasive procedures, prophylactic treatments, bleeding events and medical resources used in the follow up period. Frequency tables were generated for categorical variables, whereas continuous variables were described in summary tables as mean (SD) and median (Q1-Q3). RESULTS: Out of 1,765,675 patients, 1787 had CLD and severe TCP; 65.2% were male with a mean age of 54.7 years old. Cirrhosis was detected in 46% (n=820) of patients and 9.1% (n=163) had hepatocellular carcinoma. Invasive procedures were needed in 85.6% of patients during the follow up period. Patients undergoing procedures compared to those patients without invasive procedures presented higher rates of bleeding events (33% vs 8%, p<0.0001) and higher number of bleedings. While prophylactic platelet transfusions were given to 25.6% of patients undergoing procedures, TPO receptor agonist use was only detected in 3.1% of them. Most patients (60.9%) required at least one hospital admission during the follow up and 14.4% of admissions were due to bleeding events with a hospital length of stay of 6 (3, 9) days. CONCLUSIONS: NLP and machine learning are useful tools to describe real-world data in patients with CLD and severe TCP in Spain. Bleeding events are frequent in those patients who need invasive procedures, even receiving platelet transfusions as a prophylactic treatment, increasing the further use of medical resources. Because that, new prophylactic treatments that are not yet generalized, are needed.

Pictobana: Una herramienta para la comunicación con niñosautistas

Introducción: Los avances actuales en el campo de las TICs han permitido un importante impulso en el desarrollo de sistemas que traducen texto plano en español en pictogramas. Sin embargo, las soluciones actuales no pueden ser comprendidas por una persona con dificultades del lenguaje en Cuba, debido a que algunas terminologías no están presentes en el lenguaje cotidiano. Objetivo: Desarrollar el modelo Pictobana para el análisis semántico de un Pictotraductor que integre la semántica del lenguaje cubano. Métodos: El modelo fue desarrollado aplicando técnicas de procesamiento del lenguaje natural. Se realiza un análisis lingüístico con el objetivo de proporcionar las mejores representaciones posibles de los textos en pictogramas. Resultados: El modelo es implementado en una aplicación web que proporciona una herramienta que ayuda a promover las competencias y habilidades de comunicación a personas con dificultades del habla en Cuba y a sus familiares. Conclusiones: Las pruebas realizadas mediante experimentos y criterio de expertos, demuestran que el analizador desarrollado, aumenta la ajustabilidad de los pictogramas al contexto y a la semántica, aminorando la incoherencia y la ambigüedad semántica del futuro sistema.

Introduction: Current advances in the field of ICTs have allowed an important boost in the development of systems that allow translating plain text in Spanish into pictograms. However, the current solutions cannot be understood by a person with language difficulties in Cuba because some terminologies are not present in everyday language. Objective: To develop the Pictobana model for the semantic analysis of a Pictotranslator that integrates the semantics of the Cuban language. Methods: The model was developed by applying natural language processing techniques. A linguistic analysis was carried out with the aim of providing the best possible representations of the texts in pictograms. Results: The model is implemented in a web application that provides a tool that helps promote communication skills and abilities for people with speech difficulties and their families in Cuba. Conclusions: The tests carried out through experiments and expert criteria show that the developed analyzer increases the adjustability of the pictograms to the context and the semantics, reducing the incoherence and semantic ambiguity of the future system.

Identifying the most important data for research in the field of infectious diseases: thinking on the basis of artificial intelligence / Identificación de los datos más importantes para el desarrollo de inteligencia artificial en el campo de las enfermedades infecciosas

Objectives. Clinical data on which artificial intelligence (AI) algorithms are trained and tested provide the basis to im prove diagnosis or treatment of infectious diseases (ID). We aimed to identify important data for ID research to prioritise efforts being undertaken in AI programmes. Material and methods. We searched for 1,000 articles from high-impact ID journals on PubMed, selecting 288 of the latest articles from 10 top journals. We classified them into structured or unstructured data. Variables were homogenised and grouped into the following categories: epidemiology, ad mission, demographics, comorbidities, clinical manifestations, laboratory, microbiology, other diagnoses, treatment, out comes and other non-categorizable variables. Results. 4,488 individual variables were collected, from the 288 articles. 3,670 (81.8%) variables were classified as structured data whilst 818 (18.2%) as unstructured data. From the structured data, 2,319 (63.2%) variables were classified as direct—retrievable from electronic health records—whilst 1,351 (36.8%) were indirect. The most frequent unstructured data were related to clinical manifestations and were repeated across articles. Data on demographics, comorbidities and mi crobiology constituted the most frequent group of variables. Conclusions. This article identified that structured vari ables have comprised the most important data in research to generate knowledge in the field of ID. Extracting these data should be a priority when a medical centre intends to start an AI programme for ID. We also documented that the most important unstructured data in this field are those related to clinical manifestations. Such data could easily undergo some structuring with the use of semi-structured medical records focusing on a few symptoms (AU)

Objetivos. Los datos clínicos sobre los que se entrenan y prueban los algoritmos de inteligencia artificial (IA) proporcio nan la base para mejorar el diagnóstico o el tratamiento de las enfermedades infecciosas (EI). Nuestro objetivo es identificar datos importantes para la investigación de las enfermedades infecciosas con el fin de priorizar los esfuerzos realizados en los programas de IA. Material y métodos. Se buscaron 1.000 artículos de re vistas de EI de alto impacto en PubMed, seleccionando 288 de los últimos artículos en 10 revistas de primer nivel. Los clasifi camos en datos estructurados o no estructurados. Las variables se homogeneizaron y agruparon en las siguientes categorías: epidemiología, ingreso, demografía, comorbilidades, manifes taciones clínicas, laboratorio, microbiología, otros diagnósticos, tratamiento, desenlace y otras variables no categorizables. Resultados. Se recogieron 4.488 variables individuales, pro cedentes de 288 artículos. 3670 (81,8%) variables se clasificaron como datos estructurados, mientras que 818 (18,2%) como da tos no estructurados. De los datos estructurados, 2.319 (63,2%) variables se clasificaron como directas -recuperables a partir de historias clínicas electrónicas-, mientras que 1.351 (36,8%) fueron indirectas. Los datos no estructurados más frecuentes estaban re lacionados con las manifestaciones clínicas y se repetían en todos los artículos. Los datos sobre demografía, comorbilidades y micro biología constituyeron el grupo más frecuente de variables Conclusiones. Este artículo identificó que las variables es tructuradas han constituido los datos más importantes en la investigación para generar conocimiento en el campo de la EI. La extracción de estos datos debería ser una prioridad cuando un centro médico pretende iniciar un programa de IA para la EI (AU)

La era del big data: análisis del lenguaje natural mediante la aplicación de folksonomía / The big data era: The usefulness of folksonomy for natural language processing

Antecedentes y objetivo: Gran parte de la información médica que se deriva de la práctica clínica habitual queda recogida en forma de lenguaje natural en los informes médicos. Clásicamente, la extracción de información clínica para su posterior análisis a partir de los informes médicos requiere de la lectura y revisión manual de cada uno de ellos con la consiguiente inversión de tiempo. El objetivo de este proyecto piloto ha sido evaluar la utilidad de la folksonomía para la extracción y análisis rápido de los datos que contienen los informes médicos. Material y métodos: En este proyecto piloto hemos utilizado la folksonomía para el análisis y la rápida extracción de datos de 1.631 informes médicos de alta de hospitalización del Servicio de Nefrología del Hospital del Mar sin necesidad de crear una base de datos estructurada previamente. Resultados: A partir de determinadas preguntas sobre la práctica médica habitual (tratamiento hipoglicemiante de los pacientes diabéticos, tratamiento antihipertensivo y manejo de los inhibidores del sistema renina angiotensina durante el ingreso en nefrología y análisis de datos relacionados con la esfera emocional de los pacientes renales) la herramienta ha permitido estructurar y analizar la información contenida en texto libre en los informes de alta. Conclusiones: La aplicación de folksonomía a los informes médicos nos permite transformar la información contenida en lenguaje natural en una serie de datos estructurados y analizables de manera automática sin necesidad de proceder a la revisión manual de los mismos. (AU)

Background: A huge amount of clinical data is daily generated and it is usually filed in clinical reports as natural language. Data extraction and further analysis requires reading and manual review of each report, which is a time consuming process. With the aim to test folksonomy to quickly obtain and analyze the information contained in medial reports we set up this study. Methods and objectives:We have used folksonomy to quickly obtain and analyse data from 1631 discharge clinical reports from Nephrology Department of Hospital del Mar, without the need to create an structured database. Results: After posing some questions related to daily clinical practice (hypoglycaemic drugs used in diabetic patients, antihypertensive drugs and the use of renin angiotensin blockers during hospitalisation in the nephrology department and data related to emotional environment of patients with chronic kidney disease) this tool has allowed the conversion of unstructured information in natural language into a structured pool of data for its further analysis. Conclusions: Folksonomy allows the conversion of the information contained in clinical reports as natural language into a pool of structured data which can be further easily analysed without the need of the classical manual review of the reports. (AU)

El valor de los datos y su aplicabilidad en el Sector Sanitario / The value of data and its applicability in the Health Sector

Actualmente las noticias y/o artículos sobre la utilización de la Inteligencia Artificial (IA) y el Big Data nos están inundando y esta situación se ha agudizado con la pandemia, donde se ha dado una gran importancia a su utilización y las diversas aplicaciones en todos los sectores. Unos ámbitos tecnológicos y de oportunidades que cada vez se encuentran más presentes en nuestro día a día. El sector que más crecimiento ha experimento durante este tiempo de pandemia es, sin lugar a dudas, el sector sanitario. La imperiosa necesidad ha fomentado y agilizado el uso de estas tecnologías. La utilización de datos para poder acometer tratamientos en un breve tiempo, ver las evoluciones de las diferentes enfermedades y predecir su estado es lo que ha impulsado su utilización y donde debido a la situación cualquier ayuda era y es poca. Desde este artículo pretendemos dar una explicación de los beneficios del uso de la IA y las diferentes técnicas del Big Data, tanto en el estudio y evolución de enfermedades como en su prevención, detección, seguimiento y tratamiento (AU)

Currently news and/or articles on the use of Artificial Intelligence and the Big Data are flooding us and this situation has worsened with the pandemic, where great importance has been given to its use and the various applications in all sectors. Some areas of technology and opportunities that are increasingly are more present in our day to day. The sector that has experienced the most growth during this time of pandemic is, without a doubt, the Health sector. The imperative need has fostered and expedited the use of these technologies. The use of data to be able to undertake treatments in a short time, see the evolutions of the different diseases and predict their state is what has driven its use and where due to the situation any help was and is little. From this article we intend to give an explanation of the benefits of using the Artificial Intelligence and the different Big Data techniques, both in the study and evolution of diseases as in their prevention, detection, monitoring and treatment (AU)

The big data era: The usefulness of folksonomy for natural language processing.

BACKGROUND: A huge amount of clinical data is generated daily and it is usually filed in clinical reports as natural language. Data extraction and further analysis requires reading and manual review of each report, which is a time consuming process. With the aim to test folksonomy to quickly obtain and analyze the information contained in media reports we set up this study. METHODS AND OBJECTIVES: We have used folksonomy to quickly obtain and analyze data from 1631 discharge clinical reports from the Nephrology Department of Hospital del Mar, without the need to create a structured database. RESULTS: After posing some questions related to daily clinical practice (hypoglycaemic drugs used in diabetic patients, antihypertensive drugs and the use of renin angiotensin blockers during hospitalization in the nephrology department and data related to emotional environment of patients with chronic kidney disease) this tool has allowed the conversion of unstructured information in natural language into a structured pool of data for its further analysis. CONCLUSIONS: Folksonomy allows the conversion of the information contained in clinical reports as natural language into a pool of structured data which can be further easily analyzed without the need for the classical manual review of the reports.

The value of data and its applicability in the Health Sector.

Currently news and/or articles on the use of Artificial Intelligence and the Big Data are flooding us and this situation has worsened with the pandemic, where great importance has been given to its use and the various applications in all sectors. Some areas of technology and opportunities that are increasingly are more present in our day to day. The sector that has experienced the most growth during this time of pandemic is, without a doubt, the Health sector. The imperative need has fostered and expedited the use of these technologies. The use of data to be able to undertake treatments in a short time, see the evolutions of the different diseases and predict their state is what has driven its use and where due to the situation any help was and is little. From this article we intend to give an explanation of the benefits of using the Artificial Intelligence and the different Big Data techniques, both in the study and evolution of diseases as in their prevention, detection, monitoring and treatment.