Extreme multi-label deep neural classification of Spanish health records according to the International Classification of Diseases

111 p.Este trabajo trata sobre la minería de textos clínicos, un campo del Procesamiento del Lenguaje Natural aplicado al dominio biomédico. El objetivo es automatizar la tarea de codificación médica. Los registros electrónicos de salud (EHR) son documentos que contienen información clínica sobre la salud de unpaciente. Los diagnósticos y procedimientos médicos plasmados en la Historia Clínica Electrónica están codificados con respecto a la Clasificación Internacional de Enfermedades (CIE). De hecho, la CIE es la base para identificar estadísticas de salud internacionales y el estándar para informar enfermedades y condiciones de salud. Desde la perspectiva del aprendizaje automático, el objetivo es resolver un problema extremo de clasificación de texto de múltiples etiquetas, ya que a cada registro de salud se le asignan múltiples códigos ICD de un conjunto de más de 70 000 términos de diagnóstico. Una cantidad importante de recursos se dedican a la codificación médica, una laboriosa tarea que actualmente se realiza de forma manual. Los EHR son narraciones extensas, y los codificadores médicos revisan los registros escritos por los médicos y asignan los códigos ICD correspondientes. Los textos son técnicos ya que los médicos emplean una jerga médica especializada, aunque rica en abreviaturas, acrónimos y errores ortográficos, ya que los médicos documentan los registros mientras realizan la práctica clínica real. Paraabordar la clasificación automática de registros de salud, investigamos y desarrollamos un conjunto de técnicas de clasificación de texto de aprendizaje profundo

1998 - 2000 Bulletin

Loma Linda UniversityGraduate SchoolBulletin 1998-2000 Volume 88, Number 7, September 25, 1998https://scholarsrepository.llu.edu/gs_bulletin/1001/thumbnail.jp

Washington University Record, October 24, 1996

https://digitalcommons.wustl.edu/record/1737/thumbnail.jp

2000 - 2004 Bulletin

Bulletin of LOMA LINDA UNIVERSITYGraduate School 2000-04 Volume 90, Number 3, March 30, 2002https://scholarsrepository.llu.edu/gs_bulletin/1000/thumbnail.jp

Cholangiocarcinoma 2020: the next horizon in mechanisms and management

[EN] Cholangiocarcinoma (CCA) includes a cluster of highly heterogeneous biliary malignant tumours that can arise at any point of the biliary tree. Their incidence is increasing globally, currently accounting for ~15% of all primary liver cancers and ~3% of gastrointestinal malignancies. The silent presentation of these tumours combined with their highly aggressive nature and refractoriness to chemotherapy contribute to their alarming mortality, representing ~2% of all cancer-related deaths worldwide yearly. The current diagnosis of CCA by non- invasive approaches is not accurate enough, and histological confirmation is necessary. Furthermore, the high heterogeneity of CCAs at the genomic, epigenetic and molecular levels severely compromises the efficacy of the available therapies. In the past decade, increasing efforts have been made to understand the complexity of these tumours and to develop new diagnostic tools and therapies that might help to improve patient outcomes. In this expert Consensus Statement, which is endorsed by the European Network for the Study of Cholangiocarcinoma, we aim to summarize and critically discuss the latest advances in CCA, mostly focusing on classification, cells of origin, genetic and epigenetic abnormalities, molecular alterations, biomarker discovery and treatments. Furthermore, the horizon of CCA for the next decade from 2020 onwards is highlightedJ.M.B. received EASL Registry Awards 2016 and 2019 (European CCA Registry, ENS-CCA). J.M.B. and M.J.P. were supported by: the Spanish Ministry of Economy and Competitiveness (J.M.B.: FIS PI12/00380, FIS PI15/01132, FIS PI18/01075 and Miguel Servet Programme CON14/00129; M.J.P.: FIS PI14/00399, FIS PI17/00022 and Ramon y Cajal Programme RYC-2015-17755, co-financed by “Fondo Europeo de Desarrollo Regional” (FEDER)); ISCIII CIBERehd; “Diputación Foral de Gipuzkoa” (J.M.B: DFG15/010, DFG16/004), and BIOEF (Basque Foundation for Innovation and Health Research: EiTB Maratoia BIO15/CA/016/BD); the Department of Health of the Basque Country (M.J.P.: 2015111100; J.M.B.: 2017111010), and “Fundación Científica de la Asociación Española Contra el Cancer” (AECC Scientific Foundation) (J.M.B.). J.M.B. and J.W.V. were supported by the European Commission Horizon 2020 programme (ESCALON project 825510). The laboratory of J.B.A. is supported by competitive grants from the Danish Medical Research Council, the Danish Cancer Society, and the Novo Nordisk and A.P. Møller Foundations. J.J.G.M. and R.I.R.M. were supported by the Carlos III Institute of Health, Spain (PI16/00598 and PI18/00428) and were co-financed by the European Regional Development Fund. J.M.B. and J.J.G.M. were supported by the Ministry of Science and Innovation, Spain (SAF2016-75197-R), and the “Asociación Española Contra el Cancer”, Spain (AECC-2017). R.I.R.M. was supported by the “Centro Internacional sobre el Envejecimiento”, Spain (OLD-HEPAMARKER, 0348-CIE-6-E). A.L. received funding from the Christie Charity. M.M. was supported by the Università Politecnica delle Marche, Ancona, Italy (040020_R.SCIENT.A_2018_MARZIONI_M_STRATEGICO_2017). M.S. was supported by the Yale Liver Center Clinical and Translational Core and the Cellular and Molecular Core (DK034989 Silvio O. Conte Digestive Diseases Research Center). C.C. is supported by grants from INSERM, Université de Rennes, INCa, and ITMO Cancer AVIESAN dans le cadre du Plan Cancer (Non-coding RNA in Cancerology: Fundamental to Translational), Ligue Contre le Cancer and Région Bretagne. J.Bruix was supported by grants from Instituto de Salud Carlos III (PI18/00763), AECC (PI044031) and WCR (AICR) 16-0026. A.F. was supported by grants from ISCIII (PI13/01229 and PI18/00542). CIBERehd is funded by the Instituto de Salud Carlos III. V.C., D.M., J. Bridgewater and P.I. are members of the European Reference Network - Hepatological Diseases (ERN RARE-LIVER). J.M.B. is a collaborator of the ERN RARE-LIVER