Q3156440 (Q3156440): Difference between revisions
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(Created claim: summary (P836): PERSONALISED MEDICINE (PM) SEEKS TO IDENTIFY PERSONALISED THERAPIES THAT MAKE THE INDIVIDUALISED TREATMENT OF SPECIFIC PATIENTS SAFE AND EFFECTIVE. ONE OF THE GREAT DIFFICULTIES IN CARRYING OUT THIS CLINICAL PRACTICE EFFECTIVELY IS THAT THERE ARE CURRENTLY NO FLEXIBLE INFORMATION SYSTEMS CAPABLE OF PROVIDING ACCURATE, UP-TO-DATE AND INTERRELATED KNOWLEDGE BASED ON STRATIFIED ACCESS TO MULTIPLE SOURCES OF HETEROGENEOUS DATA. ALL THIS INFORMATION,...) |
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PERSONALISED MEDICINE (PM) SEEKS TO IDENTIFY PERSONALISED THERAPIES THAT MAKE THE INDIVIDUALISED TREATMENT OF SPECIFIC PATIENTS SAFE AND EFFECTIVE. ONE OF THE GREAT DIFFICULTIES IN CARRYING OUT THIS CLINICAL PRACTICE EFFECTIVELY IS THAT THERE ARE CURRENTLY NO FLEXIBLE INFORMATION SYSTEMS CAPABLE OF PROVIDING ACCURATE, UP-TO-DATE AND INTERRELATED KNOWLEDGE BASED ON STRATIFIED ACCESS TO MULTIPLE SOURCES OF HETEROGENEOUS DATA. ALL THIS INFORMATION, GENERATED IN EXPERIMENTAL STUDIES, CLINICAL TRIALS AND DAILY CLINICAL PRACTICE, AS WELL AS RECENTLY THROUGH BIOMEDICAL SENSORS AND LARGE OPEN AND INTERLOCKING DATA SETS (OPEN AND LINKED DATA) SHOULD BECOME AN EXTRAORDINARY SOURCE OF KNOWLEDGE FOR THE ADVANCEMENT OF PM. HOWEVER, THE PM CURRENTLY FACES GREAT CHALLENGES. IT IS NECESSARY TO INTEGRATE HETEROGENEOUS INFORMATION DISPERSED IN MULTIPLE ORIGINS, OF DIFFERENT GENRES, DOMAIN, STRUCTURE AND SCALE, WHERE ALSO PLAYS A VERY IMPORTANT ROLE THE TEXTUAL COMPONENT. In order to address these risks, the application of coORDINAD FORMATION OF INTEGRATION INTEGRATION INFORMATION TECHNOLOGY TECHNOLOGY AND TEXT AND DATA MINERY TYPE FUENTS TO FACILIT THE EXTRACTION OF ASOCIATED KNOWING._x000D_ _x000D__x000D__x000D_ is proposed in this project. the PRINCIPAL OBJECTIVE OF THE PROJECT IS DESIGNING TOOLS PERMITING A INTEGRATED AND INTELIGENT ACCESSMENT TO THE INFORMATION RELATED TO CONSEMBLY THE EXTRACTION OF USE KNOWING IN THE CONTEXT OF THE PM. (I) ASSISTANCE TO HEALTHCARE PROFESSIONALS DURING THE CLINICAL DECISION-MAKING PROCESS, (II) ACCESS TO RELEVANT INFORMATION ABOUT THEIR HEALTH STATUS TO CHRONIC AND DEPENDENT PATIENTS AND (III) SUPPORT FOR EVIDENCE-BASED TRAINING OF NEW MEDICAL STUDENTS. MORE EFFECTIVE TECHNIQUES WILL BE PROPOSED FOR OPERATIONS SUCH AS GENERATING ABSTRACTS, RECOVERING IMAGES FROM TEXT, RECOVERING INFORMATION, RECOGNISING NAMED ENTITIES, AND EXTRACTING INFORMATION FROM LARGE DATA SETS FROM BOTH SENSORS AND USING FREE ACCESS DATA SETS. TOOLS WILL BE IMPLEMENTED TO OBTAIN BIOMEDICAL KNOWLEDGE FROM, MAINLY, PUBLIC RESOURCES. AN ARCHITECTURE AND A FRAMEWORK OF WEB APPLICATIONS WILL BE DESIGNED THAT ALLOWS THE INTEGRATION OF PROCESSES AND TECHNIQUES OF TEXT AND DATA MINING AND INTEGRATION OF INFORMATION IN A FAST, UNIFORM AND REUSABLE WAY (THROUGH PLUGINS). FINALLY, INTELLIGENT TOOLS FOR USER SUPPORT WILL BE DEVELOPED IN THE THREE DEFINED SCENARIOS: DECISION MAKING FOR DIAGNOSIS AND TREATMENT, PATIENTS, AND TRAINING. IN ADDITION, EXPERIMENTS WILL BE CARRIED OUT FOR EVALUATION, BOTH EFFECTIVENESS AND USABILITY, THROUGH SYSTEMATIC EVALUATIONS AND WITH USERS. IN THE CASE OF THE FORMER, THEY WILL PARTICIPATE IN COMPETITIONS SUCH AS TREC-MEDICAL RECORDS, CLEF, TAC, DDIEXTRACTION, I2B2, BIOCREATIVE, WITH THE SHARED TASK OR BIONLP SHARED TASK. IN USER ASSESSMENTS, BOTH OPEN AND CONTROLLED ENVIRONMENTS WILL BE CONSIDERED. (English) | |||||||||||||||
| Property / summary: PERSONALISED MEDICINE (PM) SEEKS TO IDENTIFY PERSONALISED THERAPIES THAT MAKE THE INDIVIDUALISED TREATMENT OF SPECIFIC PATIENTS SAFE AND EFFECTIVE. ONE OF THE GREAT DIFFICULTIES IN CARRYING OUT THIS CLINICAL PRACTICE EFFECTIVELY IS THAT THERE ARE CURRENTLY NO FLEXIBLE INFORMATION SYSTEMS CAPABLE OF PROVIDING ACCURATE, UP-TO-DATE AND INTERRELATED KNOWLEDGE BASED ON STRATIFIED ACCESS TO MULTIPLE SOURCES OF HETEROGENEOUS DATA. ALL THIS INFORMATION, GENERATED IN EXPERIMENTAL STUDIES, CLINICAL TRIALS AND DAILY CLINICAL PRACTICE, AS WELL AS RECENTLY THROUGH BIOMEDICAL SENSORS AND LARGE OPEN AND INTERLOCKING DATA SETS (OPEN AND LINKED DATA) SHOULD BECOME AN EXTRAORDINARY SOURCE OF KNOWLEDGE FOR THE ADVANCEMENT OF PM. HOWEVER, THE PM CURRENTLY FACES GREAT CHALLENGES. IT IS NECESSARY TO INTEGRATE HETEROGENEOUS INFORMATION DISPERSED IN MULTIPLE ORIGINS, OF DIFFERENT GENRES, DOMAIN, STRUCTURE AND SCALE, WHERE ALSO PLAYS A VERY IMPORTANT ROLE THE TEXTUAL COMPONENT. In order to address these risks, the application of coORDINAD FORMATION OF INTEGRATION INTEGRATION INFORMATION TECHNOLOGY TECHNOLOGY AND TEXT AND DATA MINERY TYPE FUENTS TO FACILIT THE EXTRACTION OF ASOCIATED KNOWING._x000D_ _x000D__x000D__x000D_ is proposed in this project. the PRINCIPAL OBJECTIVE OF THE PROJECT IS DESIGNING TOOLS PERMITING A INTEGRATED AND INTELIGENT ACCESSMENT TO THE INFORMATION RELATED TO CONSEMBLY THE EXTRACTION OF USE KNOWING IN THE CONTEXT OF THE PM. (I) ASSISTANCE TO HEALTHCARE PROFESSIONALS DURING THE CLINICAL DECISION-MAKING PROCESS, (II) ACCESS TO RELEVANT INFORMATION ABOUT THEIR HEALTH STATUS TO CHRONIC AND DEPENDENT PATIENTS AND (III) SUPPORT FOR EVIDENCE-BASED TRAINING OF NEW MEDICAL STUDENTS. MORE EFFECTIVE TECHNIQUES WILL BE PROPOSED FOR OPERATIONS SUCH AS GENERATING ABSTRACTS, RECOVERING IMAGES FROM TEXT, RECOVERING INFORMATION, RECOGNISING NAMED ENTITIES, AND EXTRACTING INFORMATION FROM LARGE DATA SETS FROM BOTH SENSORS AND USING FREE ACCESS DATA SETS. TOOLS WILL BE IMPLEMENTED TO OBTAIN BIOMEDICAL KNOWLEDGE FROM, MAINLY, PUBLIC RESOURCES. AN ARCHITECTURE AND A FRAMEWORK OF WEB APPLICATIONS WILL BE DESIGNED THAT ALLOWS THE INTEGRATION OF PROCESSES AND TECHNIQUES OF TEXT AND DATA MINING AND INTEGRATION OF INFORMATION IN A FAST, UNIFORM AND REUSABLE WAY (THROUGH PLUGINS). FINALLY, INTELLIGENT TOOLS FOR USER SUPPORT WILL BE DEVELOPED IN THE THREE DEFINED SCENARIOS: DECISION MAKING FOR DIAGNOSIS AND TREATMENT, PATIENTS, AND TRAINING. IN ADDITION, EXPERIMENTS WILL BE CARRIED OUT FOR EVALUATION, BOTH EFFECTIVENESS AND USABILITY, THROUGH SYSTEMATIC EVALUATIONS AND WITH USERS. IN THE CASE OF THE FORMER, THEY WILL PARTICIPATE IN COMPETITIONS SUCH AS TREC-MEDICAL RECORDS, CLEF, TAC, DDIEXTRACTION, I2B2, BIOCREATIVE, WITH THE SHARED TASK OR BIONLP SHARED TASK. IN USER ASSESSMENTS, BOTH OPEN AND CONTROLLED ENVIRONMENTS WILL BE CONSIDERED. (English) / rank | |||||||||||||||
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| Property / summary: PERSONALISED MEDICINE (PM) SEEKS TO IDENTIFY PERSONALISED THERAPIES THAT MAKE THE INDIVIDUALISED TREATMENT OF SPECIFIC PATIENTS SAFE AND EFFECTIVE. ONE OF THE GREAT DIFFICULTIES IN CARRYING OUT THIS CLINICAL PRACTICE EFFECTIVELY IS THAT THERE ARE CURRENTLY NO FLEXIBLE INFORMATION SYSTEMS CAPABLE OF PROVIDING ACCURATE, UP-TO-DATE AND INTERRELATED KNOWLEDGE BASED ON STRATIFIED ACCESS TO MULTIPLE SOURCES OF HETEROGENEOUS DATA. ALL THIS INFORMATION, GENERATED IN EXPERIMENTAL STUDIES, CLINICAL TRIALS AND DAILY CLINICAL PRACTICE, AS WELL AS RECENTLY THROUGH BIOMEDICAL SENSORS AND LARGE OPEN AND INTERLOCKING DATA SETS (OPEN AND LINKED DATA) SHOULD BECOME AN EXTRAORDINARY SOURCE OF KNOWLEDGE FOR THE ADVANCEMENT OF PM. HOWEVER, THE PM CURRENTLY FACES GREAT CHALLENGES. IT IS NECESSARY TO INTEGRATE HETEROGENEOUS INFORMATION DISPERSED IN MULTIPLE ORIGINS, OF DIFFERENT GENRES, DOMAIN, STRUCTURE AND SCALE, WHERE ALSO PLAYS A VERY IMPORTANT ROLE THE TEXTUAL COMPONENT. In order to address these risks, the application of coORDINAD FORMATION OF INTEGRATION INTEGRATION INFORMATION TECHNOLOGY TECHNOLOGY AND TEXT AND DATA MINERY TYPE FUENTS TO FACILIT THE EXTRACTION OF ASOCIATED KNOWING._x000D_ _x000D__x000D__x000D_ is proposed in this project. the PRINCIPAL OBJECTIVE OF THE PROJECT IS DESIGNING TOOLS PERMITING A INTEGRATED AND INTELIGENT ACCESSMENT TO THE INFORMATION RELATED TO CONSEMBLY THE EXTRACTION OF USE KNOWING IN THE CONTEXT OF THE PM. (I) ASSISTANCE TO HEALTHCARE PROFESSIONALS DURING THE CLINICAL DECISION-MAKING PROCESS, (II) ACCESS TO RELEVANT INFORMATION ABOUT THEIR HEALTH STATUS TO CHRONIC AND DEPENDENT PATIENTS AND (III) SUPPORT FOR EVIDENCE-BASED TRAINING OF NEW MEDICAL STUDENTS. MORE EFFECTIVE TECHNIQUES WILL BE PROPOSED FOR OPERATIONS SUCH AS GENERATING ABSTRACTS, RECOVERING IMAGES FROM TEXT, RECOVERING INFORMATION, RECOGNISING NAMED ENTITIES, AND EXTRACTING INFORMATION FROM LARGE DATA SETS FROM BOTH SENSORS AND USING FREE ACCESS DATA SETS. TOOLS WILL BE IMPLEMENTED TO OBTAIN BIOMEDICAL KNOWLEDGE FROM, MAINLY, PUBLIC RESOURCES. AN ARCHITECTURE AND A FRAMEWORK OF WEB APPLICATIONS WILL BE DESIGNED THAT ALLOWS THE INTEGRATION OF PROCESSES AND TECHNIQUES OF TEXT AND DATA MINING AND INTEGRATION OF INFORMATION IN A FAST, UNIFORM AND REUSABLE WAY (THROUGH PLUGINS). FINALLY, INTELLIGENT TOOLS FOR USER SUPPORT WILL BE DEVELOPED IN THE THREE DEFINED SCENARIOS: DECISION MAKING FOR DIAGNOSIS AND TREATMENT, PATIENTS, AND TRAINING. IN ADDITION, EXPERIMENTS WILL BE CARRIED OUT FOR EVALUATION, BOTH EFFECTIVENESS AND USABILITY, THROUGH SYSTEMATIC EVALUATIONS AND WITH USERS. IN THE CASE OF THE FORMER, THEY WILL PARTICIPATE IN COMPETITIONS SUCH AS TREC-MEDICAL RECORDS, CLEF, TAC, DDIEXTRACTION, I2B2, BIOCREATIVE, WITH THE SHARED TASK OR BIONLP SHARED TASK. IN USER ASSESSMENTS, BOTH OPEN AND CONTROLLED ENVIRONMENTS WILL BE CONSIDERED. (English) / qualifier | |||||||||||||||
point in time: 12 October 2021
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Revision as of 15:15, 12 October 2021
Project Q3156440 in Spain
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | No label defined |
Project Q3156440 in Spain |
Statements
5,992,113.6 Euro
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7,490,142.0 Euro
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80.0 percent
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1 January 2014
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31 December 2017
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UNIVERSIDAD DE HUELVA
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37°13'39.97"N, 6°53'35.63"W
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21055
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LA MEDICINA PERSONALIZADA (PM, PERSONALIZED MEDICINE) BUSCA LA IDENTIFICACION DE TERAPIAS PERSONALIZADAS QUE HAGAN SEGURO Y EFECTIVO EL TRATAMIENTO INDIVIDUALIZADO DE PACIENTES ESPECIFICOS. UNA DE LAS GRANDES DIFICULTADES PARA LLEVAR A CABO ESTA PRACTICA CLINICA DE FORMA EFECTIVA ES QUE EN LA ACTUALIDAD NO EXISTEN SISTEMAS FLEXIBLES DE INFORMACION CAPACES DE PROPORCIONAR CONOCIMIENTO PRECISO, ACTUALIZADO E INTERRELACIONADO BASADO EN EL ACCESO ESTRATIFICADO A MULTIPLES ORIGENES DE DATOS DE TIPO HETEROGENEO. TODA ESTA INFORMACION, GENERADA EN ESTUDIOS EXPERIMENTALES, ENSAYOS CLINICOS Y EN LA PRACTICA CLINICA DIARIA, ASI COMO RECIENTEMENTE A TRAVES DE SENSORES BIOMEDICOS Y GRANDES CONJUNTOS DE DATOS DE LIBRE ACCESO Y ENTRELAZADO (OPEN Y LINKED DATA) DEBERIA CONVERTIRSE EN UNA FUENTE EXTRAORDINARIA DE CONOCIMIENTO PARA EL AVANCE DE LA PM. SIN EMBARGO, LA PM SE ENFRENTA EN LA ACTUALIDAD A GRANDES RETOS. ES NECESARIO INTEGRAR INFORMACION HETEROGENEA DISPERSA EN MULTIPLES ORIGENES, DE DIFERENTES GENERO, DOMINIO, ESTRUCTURA Y ESCALA, DONDE ADEMAS JUEGA UN PAPEL MUY IMPORTANTE LA COMPONENTE TEXTUAL. PARA AFRONTAR ESTOS RETOS, EN ESTE PROYECTO SE PROPONE LA APLICACION DE FORMA COORDINADA DE TECNICAS DE INTEGRACION DE INFORMACION PARA CONSEGUIR ABARCAR FUENTES DE TIPO HETEROGENEO Y DE MINERIA DE TEXTOS Y DATOS PARA FACILITAR LA EXTRACCION DE CONOCIMIENTO ASOCIADO._x000D_ _x000D_ EL OBJETIVO PRINCIPAL DEL PROYECTO ES DISEÑAR HERRAMIENTAS QUE PERMITAN UN ACCESO INTEGRADO E INTELIGENTE A LA INFORMACION RELACIONADA PARA CONSEGUIR LA EXTRACCION DE CONOCIMIENTO UTIL EN EL CONTEXTO DE LA PM. SE PROPONEN TRES ESCENARIOS DE USO: (I) LA ASISTENCIA A LOS PROFESIONALES SANITARIOS DURANTE EL PROCESO DE TOMA DE DECISIONES DE AMBITO CLINICO, (II) EL ACCESO A INFORMACION RELEVANTE SOBRE SU ESTADO DE SALUD A PACIENTES CRONICOS Y DEPENDIENTES Y (III) EL SOPORTE A LA FORMACION BASADA EN LA EVIDENCIA DE LOS NUEVOS ESTUDIANTES DE MEDICINA. SE PROPONDRAN TECNICAS MAS EFECTIVAS PARA OPERACIONES COMO GENERACION DE RESUMENES, RECUPERACION DE IMAGENES A PARTIR DE TEXTO, RECUPERACION DE INFORMACION, RECONOCIMIENTO DE ENTIDADES NOMBRADAS, Y EXTRACCION DE INFORMACION DE GRANDES CONJUNTOS DE DATOS TANTO PROVENIENTES DE SENSORES COMO UTILIZANDO CONJUNTOS DE DATOS DE LIBRE ACCESO. SE IMPLEMENTARAN HERRAMIENTAS QUE PERMITAN OBTENER CONOCIMIENTO BIOMEDICO A PARTIR DE, PRINCIPALMENTE, RECURSOS PUBLICOS. SE DISEÑARA UNA ARQUITECTURA Y UN FRAMEWORK DE APLICACIONES WEB QUE PERMITA LA INTEGRACION DE PROCESOS Y TECNICAS DE MINERIA DE TEXTO Y DATOS E INTEGRACION DE INFORMACION DE UNA FORMA RAPIDA, UNIFORME Y REUTILIZABLE (MEDIANTE PLUGINS). FINALMENTE, SE DESARROLLARAN HERRAMIENTAS INTELIGENTES PARA EL SOPORTE AL USUARIO EN LOS TRES ESCENARIOS DEFINIDOS: TOMA DE DECISIONES PARA EL DIAGNOSTICO Y TRATAMIENTO, PACIENTES, Y FORMACION. ADEMAS, SE LLEVARAN A CABO EXPERIMENTOS PARA LA EVALUACION, TANTO DE EFECTIVIDAD COMO DE USABILIDAD, MEDIANTE EVALUACIONES SISTEMATICAS Y CON USUARIOS. EN EL CASO DE LAS PRIMERAS, SE PARTICIPARA EN COMPETICIONES COMO TREC-MEDICAL RECORDS, CLEF, TAC, DDIEXTRACTION, I2B2, BIOCREATIVE, CONLL SHARED TASK O BIONLP SHARED TASK. EN LAS EVALUACIONES CON USUARIOS, SE CONSIDERARAN TANTO ENTORNOS ABIERTOS COMO CONTROLADOS. (Spanish)
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PERSONALISED MEDICINE (PM) SEEKS TO IDENTIFY PERSONALISED THERAPIES THAT MAKE THE INDIVIDUALISED TREATMENT OF SPECIFIC PATIENTS SAFE AND EFFECTIVE. ONE OF THE GREAT DIFFICULTIES IN CARRYING OUT THIS CLINICAL PRACTICE EFFECTIVELY IS THAT THERE ARE CURRENTLY NO FLEXIBLE INFORMATION SYSTEMS CAPABLE OF PROVIDING ACCURATE, UP-TO-DATE AND INTERRELATED KNOWLEDGE BASED ON STRATIFIED ACCESS TO MULTIPLE SOURCES OF HETEROGENEOUS DATA. ALL THIS INFORMATION, GENERATED IN EXPERIMENTAL STUDIES, CLINICAL TRIALS AND DAILY CLINICAL PRACTICE, AS WELL AS RECENTLY THROUGH BIOMEDICAL SENSORS AND LARGE OPEN AND INTERLOCKING DATA SETS (OPEN AND LINKED DATA) SHOULD BECOME AN EXTRAORDINARY SOURCE OF KNOWLEDGE FOR THE ADVANCEMENT OF PM. HOWEVER, THE PM CURRENTLY FACES GREAT CHALLENGES. IT IS NECESSARY TO INTEGRATE HETEROGENEOUS INFORMATION DISPERSED IN MULTIPLE ORIGINS, OF DIFFERENT GENRES, DOMAIN, STRUCTURE AND SCALE, WHERE ALSO PLAYS A VERY IMPORTANT ROLE THE TEXTUAL COMPONENT. In order to address these risks, the application of coORDINAD FORMATION OF INTEGRATION INTEGRATION INFORMATION TECHNOLOGY TECHNOLOGY AND TEXT AND DATA MINERY TYPE FUENTS TO FACILIT THE EXTRACTION OF ASOCIATED KNOWING._x000D_ _x000D__x000D__x000D_ is proposed in this project. the PRINCIPAL OBJECTIVE OF THE PROJECT IS DESIGNING TOOLS PERMITING A INTEGRATED AND INTELIGENT ACCESSMENT TO THE INFORMATION RELATED TO CONSEMBLY THE EXTRACTION OF USE KNOWING IN THE CONTEXT OF THE PM. (I) ASSISTANCE TO HEALTHCARE PROFESSIONALS DURING THE CLINICAL DECISION-MAKING PROCESS, (II) ACCESS TO RELEVANT INFORMATION ABOUT THEIR HEALTH STATUS TO CHRONIC AND DEPENDENT PATIENTS AND (III) SUPPORT FOR EVIDENCE-BASED TRAINING OF NEW MEDICAL STUDENTS. MORE EFFECTIVE TECHNIQUES WILL BE PROPOSED FOR OPERATIONS SUCH AS GENERATING ABSTRACTS, RECOVERING IMAGES FROM TEXT, RECOVERING INFORMATION, RECOGNISING NAMED ENTITIES, AND EXTRACTING INFORMATION FROM LARGE DATA SETS FROM BOTH SENSORS AND USING FREE ACCESS DATA SETS. TOOLS WILL BE IMPLEMENTED TO OBTAIN BIOMEDICAL KNOWLEDGE FROM, MAINLY, PUBLIC RESOURCES. AN ARCHITECTURE AND A FRAMEWORK OF WEB APPLICATIONS WILL BE DESIGNED THAT ALLOWS THE INTEGRATION OF PROCESSES AND TECHNIQUES OF TEXT AND DATA MINING AND INTEGRATION OF INFORMATION IN A FAST, UNIFORM AND REUSABLE WAY (THROUGH PLUGINS). FINALLY, INTELLIGENT TOOLS FOR USER SUPPORT WILL BE DEVELOPED IN THE THREE DEFINED SCENARIOS: DECISION MAKING FOR DIAGNOSIS AND TREATMENT, PATIENTS, AND TRAINING. IN ADDITION, EXPERIMENTS WILL BE CARRIED OUT FOR EVALUATION, BOTH EFFECTIVENESS AND USABILITY, THROUGH SYSTEMATIC EVALUATIONS AND WITH USERS. IN THE CASE OF THE FORMER, THEY WILL PARTICIPATE IN COMPETITIONS SUCH AS TREC-MEDICAL RECORDS, CLEF, TAC, DDIEXTRACTION, I2B2, BIOCREATIVE, WITH THE SHARED TASK OR BIONLP SHARED TASK. IN USER ASSESSMENTS, BOTH OPEN AND CONTROLLED ENVIRONMENTS WILL BE CONSIDERED. (English)
12 October 2021
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Palos de la Frontera
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Identifiers
TIN2013-47153-C3-2-R
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