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)

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.