Gestión del conocimiento médico incorporación de técnicas informáticas inteligentes en las actividades de diagnóstico y pronóstico médico
La presente tesis de maestría “GESTIÓN DEL CONOCIMIENTO MÉDICO: Incorporación de técnicas informáticas inteligentes en las actividades de asistencia sanitaria” tiene como objetivo general mostrar un marco de integracic: de diferentes métodos y técnicas de Inteligencia Artificial en pro de beneficiar...
- Autores:
-
Bohada Jaime, John Alexander
- Tipo de recurso:
- Fecha de publicación:
- 2005
- Institución:
- Universidad Autónoma de Bucaramanga - UNAB
- Repositorio:
- Repositorio UNAB
- Idioma:
- OAI Identifier:
- oai:repository.unab.edu.co:20.500.12749/25833
- Acceso en línea:
- http://hdl.handle.net/20.500.12749/25833
- Palabra clave:
- Computer sciences
Systems engineer
Artificial intelligence
Medical prognosis
Medical assistance
Medical knowledge
Decision making
Data mining
Bayesian statistical decision theory
Ciencias computacionales
Ingeniería de sistemas
Inteligencia artificial
Toma de decisiones
Minería de datos
Teoría bayesiana de decisiones estadísticas
Pronostico médico
Asistencia médica
Conocimiento médico
- Rights
- License
- http://creativecommons.org/licenses/by-nc-nd/2.5/co/
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| dc.title.spa.fl_str_mv |
Gestión del conocimiento médico incorporación de técnicas informáticas inteligentes en las actividades de diagnóstico y pronóstico médico |
| dc.title.translated.spa.fl_str_mv |
Medical knowledge management incorporating intelligent computing techniques into medical diagnosis and prognosis activities |
| title |
Gestión del conocimiento médico incorporación de técnicas informáticas inteligentes en las actividades de diagnóstico y pronóstico médico |
| spellingShingle |
Gestión del conocimiento médico incorporación de técnicas informáticas inteligentes en las actividades de diagnóstico y pronóstico médico Computer sciences Systems engineer Artificial intelligence Medical prognosis Medical assistance Medical knowledge Decision making Data mining Bayesian statistical decision theory Ciencias computacionales Ingeniería de sistemas Inteligencia artificial Toma de decisiones Minería de datos Teoría bayesiana de decisiones estadísticas Pronostico médico Asistencia médica Conocimiento médico |
| title_short |
Gestión del conocimiento médico incorporación de técnicas informáticas inteligentes en las actividades de diagnóstico y pronóstico médico |
| title_full |
Gestión del conocimiento médico incorporación de técnicas informáticas inteligentes en las actividades de diagnóstico y pronóstico médico |
| title_fullStr |
Gestión del conocimiento médico incorporación de técnicas informáticas inteligentes en las actividades de diagnóstico y pronóstico médico |
| title_full_unstemmed |
Gestión del conocimiento médico incorporación de técnicas informáticas inteligentes en las actividades de diagnóstico y pronóstico médico |
| title_sort |
Gestión del conocimiento médico incorporación de técnicas informáticas inteligentes en las actividades de diagnóstico y pronóstico médico |
| dc.creator.fl_str_mv |
Bohada Jaime, John Alexander |
| dc.contributor.advisor.none.fl_str_mv |
Pinzón, Yoan José |
| dc.contributor.author.none.fl_str_mv |
Bohada Jaime, John Alexander |
| dc.contributor.cvlac.spa.fl_str_mv |
Bohada Jaime, John Alexander [0001392883] |
| dc.subject.keywords.spa.fl_str_mv |
Computer sciences Systems engineer Artificial intelligence Medical prognosis Medical assistance Medical knowledge Decision making Data mining Bayesian statistical decision theory |
| topic |
Computer sciences Systems engineer Artificial intelligence Medical prognosis Medical assistance Medical knowledge Decision making Data mining Bayesian statistical decision theory Ciencias computacionales Ingeniería de sistemas Inteligencia artificial Toma de decisiones Minería de datos Teoría bayesiana de decisiones estadísticas Pronostico médico Asistencia médica Conocimiento médico |
| dc.subject.lemb.spa.fl_str_mv |
Ciencias computacionales Ingeniería de sistemas Inteligencia artificial Toma de decisiones Minería de datos Teoría bayesiana de decisiones estadísticas |
| dc.subject.proposal.spa.fl_str_mv |
Pronostico médico Asistencia médica Conocimiento médico |
| description |
La presente tesis de maestría “GESTIÓN DEL CONOCIMIENTO MÉDICO: Incorporación de técnicas informáticas inteligentes en las actividades de asistencia sanitaria” tiene como objetivo general mostrar un marco de integracic: de diferentes métodos y técnicas de Inteligencia Artificial en pro de beneficiar el proceso de toma de decisiones tanto diagnósticas, de tratamiento y pronosticas en el campo de la asistencia médica. En la primera parte de la tesis presenta un marco introductorio donde se indica la motivación, objetivos y la estructura del como esta organizada esta tesis. La segunda parte corresponde al marco conceptual de modelado ce conocimiento médico utilizado en los procesos de toma de decisiones computarizadas. Su base esta centrada en la exposición de diferentes aspectos ce la asistencia médica, los diferentes acercamientos utilizados en este campo, seguido de la presentación y análisis de diferentes modelos y técnicas aplicables a la gestión del conocimiento médico. Una tercera parte se enfocada a la presentación del modelo DTP (Diagnóstico, Tratamiento y Pronóstico) para la gestión del conocimiento en la asistencia médica, su arquitectura, método y técnicas aplicadas para alcanzar la meta del soporte a la toma de decisiones en este dominio. Esta pare se encuentra soportada por la explicación del conjunto de pruebas realizadas al modelo para demostrar su utilidad y validez. |
| publishDate |
2005 |
| dc.date.issued.none.fl_str_mv |
2005 |
| dc.date.accessioned.none.fl_str_mv |
2024-07-31T16:40:48Z |
| dc.date.available.none.fl_str_mv |
2024-07-31T16:40:48Z |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.local.spa.fl_str_mv |
Tesis |
| dc.type.hasversion.none.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12749/25833 |
| dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional UNAB |
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repourl:https://repository.unab.edu.co |
| url |
http://hdl.handle.net/20.500.12749/25833 |
| identifier_str_mv |
reponame:Repositorio Institucional UNAB repourl:https://repository.unab.edu.co |
| dc.relation.references.spa.fl_str_mv |
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Natic'al Higo Blood Pressure Education Program, JNC7 Express, The Seventh Repc't of the Joint National Committee on: Prevention, Detection, Evaluation, and Treatment oí High Blood Pressure, NIH publication (3-5233): 2003. G. Scnwartz. et al., lnstitute for Clinical Systems Improvement ICSI, Health Care Guie; line: Hipertensión Diagnosis and Treatment, 2003. J. Ce oc, L. Bauzidi, Cases Based Reasoning Decisión Support System for use in med cine, Upgraded II (1): 2001 pp. 30-35. |
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Pinzón, Yoan José1912e3d2-f5f2-4cb1-be92-fef030e3302fBohada Jaime, John Alexanderd68ace0e-b717-41d7-b1ed-6c3618bbb6e2Bohada Jaime, John Alexander [0001392883]Bucaramanga (Santander, Colombia)UNAB Campus Bucaramanga2024-07-31T16:40:48Z2024-07-31T16:40:48Z2005http://hdl.handle.net/20.500.12749/25833reponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coLa presente tesis de maestría “GESTIÓN DEL CONOCIMIENTO MÉDICO: Incorporación de técnicas informáticas inteligentes en las actividades de asistencia sanitaria” tiene como objetivo general mostrar un marco de integracic: de diferentes métodos y técnicas de Inteligencia Artificial en pro de beneficiar el proceso de toma de decisiones tanto diagnósticas, de tratamiento y pronosticas en el campo de la asistencia médica. En la primera parte de la tesis presenta un marco introductorio donde se indica la motivación, objetivos y la estructura del como esta organizada esta tesis. La segunda parte corresponde al marco conceptual de modelado ce conocimiento médico utilizado en los procesos de toma de decisiones computarizadas. Su base esta centrada en la exposición de diferentes aspectos ce la asistencia médica, los diferentes acercamientos utilizados en este campo, seguido de la presentación y análisis de diferentes modelos y técnicas aplicables a la gestión del conocimiento médico. Una tercera parte se enfocada a la presentación del modelo DTP (Diagnóstico, Tratamiento y Pronóstico) para la gestión del conocimiento en la asistencia médica, su arquitectura, método y técnicas aplicadas para alcanzar la meta del soporte a la toma de decisiones en este dominio. Esta pare se encuentra soportada por la explicación del conjunto de pruebas realizadas al modelo para demostrar su utilidad y validez.Instituto Tecnológico de Estudios Superiores de Monterrey (ITESM)Primera parte.............................................................................................................................................................................................10 Capitulo 1: introducción...................................................................................................................................................................... 10 1.1 motivación.......................................................................................................................................................................................10 1.2 objetivos--..........................................................................................................................................................................................11 1.3 organización de la tesis............................................................................................................................................................11 Segunda parte...................................................................................................................................................................................13 2 capitulo 2: estado del arte............................................................................................................................................................ 13 2.1 asistencia médica............................................................................................................................................................................ 13 2.1.1 diagnóstico y tratamiento médico-.......................................................................................................................................13 2.1.2 pronóstico médico-............................................................................................................................................................14 2.2 toma de decisiones en la asistencia médica........................................................................................................................ 16 2.3 sistemas de soporte a la toma de decisiones..............................................................................................................................18 2.3.1 sstd en el diagnóstico y tratamiento médico.........................................................................................................................20 2.3.2 sstd en el pronóstico médico ..................................................................................................................................................26 3 capitulo 3: gestión del conocimiento...........................................................................................................................................-30 3.1 adquisición del conocimiento y aprendizaje Automático........................................................................................................30 3.1.1 descubrimiento de conocimiento en bases de datos........................................................................................................32 3.1.2 minería de datos............................................................................................................................................................................34 3.1.3 aprendizaje automático............................................................................................................................................................36 3.1.3.1 inducción de reglas...................................................................................................................................................................37 3.1.3.2 inducción en árboles de decisión.........................................................................................................................................38 3.1.3.3 inducción por clasificación bayesiana.................................................................................................................................39 3.1.3.4 inducción por vecindad............................................................................................................................................................ 40 3.2 representación del conocimiento................................................................................................................................................40 3.2.1 árdeles de decisión y gratos de decisión............................................................................................................................... 41 3.2.2 regias............................................................................................................................................................................................ 42 3.2.3 redes semánticas y frames 43............................................................................................................................................................ 3.2.4 razonamiento basado en casos.............................................................................................................................................44 3.2.5 redes bayesianas........................................................................................................................................................................ 46 3.2.6 guías de práctica clínica............................................................................................................................................................ 48 Tercera parte............................................................................................................................................................................................. 53 4 caftulc 4: modelación del conocimiento médico.............................................................................................................................53 4.1 el modelo dtp................................................................................................................................................................................ 53 4.2 procesos del modelo dtp.............................................................................................................................................................53 4.3 arquitectura del modelo dtp............................................................................................................................................................. 55 4.3.1 diagnóstico.......................................................................................................................................................................................55 4.3.2 terapia —....................................................................................................................................................................................... 63 5 capitulo 5: experimentación............................................................................................................................................................ 66 5.1 marco de experimentación............................................................................................................................................................66 5.2 experimento uno.............................................................................................................................................................................. 66 5.3 experimento dos............................................................................................................................................................................. 75 7 conclusiones........................................................................................................................................................................................ 77 8 referencias bibliográficas-............................................................................................................................................................ 79MaestríaThe present master's thesis "MEDICAL KNOWLEDGE MANAGEMENT: Incorporation of intelligent computer techniques in healthcare activities" has as its general objective to show a framework of integration of different methods and techniques of Artificial Intelligence in order to benefit the decision-making process both diagnostic, treatment and prognostic in the field of medical care. In the first part of the thesis, an introductory framework is presented where the motivation, objectives and the structure of how this thesis is organized are indicated. The second part corresponds to the conceptual framework of modeling of medical knowledge used in computerized decision-making processes. Its basis is centered on the exposition of different aspects of medical care, the different approaches used in this field, followed by the presentation and analysis of different models and techniques applicable to medical knowledge management. A third part focuses on the presentation of the DTP model (Diagnosis, Treatment and Prognosis) for knowledge management in healthcare, its architecture, method and techniques applied to achieve the goal of supporting decision making in this domain. This part is supported by the explanation of the set of tests performed on the model to demonstrate its usefulness and validity.Modalidad Presencialhttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Gestión del conocimiento médico incorporación de técnicas informáticas inteligentes en las actividades de diagnóstico y pronóstico médicoMedical knowledge management incorporating intelligent computing techniques into medical diagnosis and prognosis activitiesMagíster en en Ciencias ComputacionalesUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaMaestría en Ciencias Computacionalesinfo:eu-repo/semantics/masterThesisTesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TMComputer sciencesSystems engineerArtificial intelligenceMedical prognosisMedical assistanceMedical knowledgeDecision makingData miningBayesian statistical decision theoryCiencias computacionalesIngeniería de sistemasInteligencia artificialToma de decisionesMinería de datosTeoría bayesiana de decisiones estadísticasPronostico médicoAsistencia médicaConocimiento médicoMatjaz Kukar. 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Bauzidi, Cases Based Reasoning Decisión Support System for use in med cine, Upgraded II (1): 2001 pp. 30-35.ORIGINAL2005_Tesis_Jhon_Alexander_Bohada_OCR.pdf2005_Tesis_Jhon_Alexander_Bohada_OCR.pdfTesisapplication/pdf22879233https://repository.unab.edu.co/bitstream/20.500.12749/25833/1/2005_Tesis_Jhon_Alexander_Bohada_OCR.pdfda01fa057d5e5345b08f217812fb84acMD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8829https://repository.unab.edu.co/bitstream/20.500.12749/25833/2/license.txt3755c0cfdb77e29f2b9125d7a45dd316MD52open accessTHUMBNAIL2005_Tesis_Jhon_Alexander_Bohada_OCR.pdf.jpg2005_Tesis_Jhon_Alexander_Bohada_OCR.pdf.jpgIM Thumbnailimage/jpeg6962https://repository.unab.edu.co/bitstream/20.500.12749/25833/3/2005_Tesis_Jhon_Alexander_Bohada_OCR.pdf.jpgfbb3850e1d5e2c14230ca4bc25e79f52MD53open access20.500.12749/25833oai:repository.unab.edu.co:20.500.12749/258332024-07-31 22:00:19.355open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.coRUwoTE9TKSBBVVRPUihFUyksIG1hbmlmaWVzdGEobWFuaWZlc3RhbW9zKSBxdWUgbGEgb2JyYSBvYmpldG8gZGUgbGEgcHJlc2VudGUgYXV0b3JpemFjacOzbiBlcyBvcmlnaW5hbCB5IGxhIHJlYWxpesOzIHNpbiB2aW9sYXIgbyB1c3VycGFyIGRlcmVjaG9zIGRlIGF1dG9yIGRlIHRlcmNlcm9zLCBwb3IgbG8gdGFudG8sIGxhIG9icmEgZXMgZGUgZXhjbHVzaXZhIGF1dG9yw61hIHkgdGllbmUgbGEgdGl0dWxhcmlkYWQgc29icmUgbGEgbWlzbWEuCgpFbiBjYXNvIGRlIHByZXNlbnRhcnNlIGN1YWxxdWllciByZWNsYW1hY2nDs24gbyBhY2Npw7NuIHBvciBwYXJ0ZSBkZSB1biB0ZXJjZXJvIGVuIGN1YW50byBhIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBzb2JyZSBsYSBvYnJhIGVuIGN1ZXN0acOzbi4gRWwgQVVUT1IgYXN1bWlyw6EgdG9kYSBsYSByZXNwb25zYWJpbGlkYWQsIHkgc2FsZHLDoSBlbiBkZWZlbnNhIGRlIGxvcyBkZXJlY2hvcyBhcXXDrSBhdXRvcml6YWRvcywgcGFyYSB0b2RvcyBsb3MgZWZlY3RvcyBsYSBVTkFCIGFjdMO6YSBjb21vIHVuIHRlcmNlcm8gZGUgYnVlbmEgZmUuCgpFbCBBVVRPUiBhdXRvcml6YSBhIGxhIFVuaXZlcnNpZGFkIEF1dMOzbm9tYSBkZSBCdWNhcmFtYW5nYSBwYXJhIHF1ZSBlbiBsb3MgdMOpcm1pbm9zIGVzdGFibGVjaWRvcyBlbiBsYSBMZXkgMjMgZGUgMTk4MiwgTGV5IDQ0IGRlIDE5OTMsIERlY2lzacOzbiBBbmRpbmEgMzUxIGRlIDE5OTMgeSBkZW3DoXMgbm9ybWFzIGdlbmVyYWxlcyBzb2JyZSBsYSBtYXRlcmlhLCB1dGlsaWNlIGxhIG9icmEgb2JqZXRvIGRlIGxhIHByZXNlbnRlIGF1dG9yaXphY2nDs24uCg== |