Differential diagnosis of hemorrhagic fevers using ARTMAP
The differential diagnosis of endemic hemorrhagic fevers in tropical countries is by no means an easy task for medical practitioners. Several diseases often overlap with others in terms of signs and symptoms, thus making this diagnosis a difficult, error-prone process. Machine Learning algorithms po...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2012
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9102
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9102
- Palabra clave:
- ARTMAP
Dengue
Differential diagnosis
Hemorrhagic fever
Leptospirosis
Machine learning
Malaria
Neural networks
Artificial intelligence
Diseases
Learning algorithms
Learning systems
Neural networks
Patient monitoring
Pattern recognition
Statistical tests
ARTMAP
Dengue
Differential diagnosis
Hemorrhagic fever
Leptospirosis
Malaria
Diagnosis
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| id |
UTB2_602f901c9a9240806f422757a14c0708 |
|---|---|
| oai_identifier_str |
oai:repositorio.utb.edu.co:20.500.12585/9102 |
| network_acronym_str |
UTB2 |
| network_name_str |
Repositorio Institucional UTB |
| repository_id_str |
|
| dc.title.none.fl_str_mv |
Differential diagnosis of hemorrhagic fevers using ARTMAP |
| title |
Differential diagnosis of hemorrhagic fevers using ARTMAP |
| spellingShingle |
Differential diagnosis of hemorrhagic fevers using ARTMAP ARTMAP Dengue Differential diagnosis Hemorrhagic fever Leptospirosis Machine learning Malaria Neural networks Artificial intelligence Diseases Learning algorithms Learning systems Neural networks Patient monitoring Pattern recognition Statistical tests ARTMAP Dengue Differential diagnosis Hemorrhagic fever Leptospirosis Malaria Diagnosis |
| title_short |
Differential diagnosis of hemorrhagic fevers using ARTMAP |
| title_full |
Differential diagnosis of hemorrhagic fevers using ARTMAP |
| title_fullStr |
Differential diagnosis of hemorrhagic fevers using ARTMAP |
| title_full_unstemmed |
Differential diagnosis of hemorrhagic fevers using ARTMAP |
| title_sort |
Differential diagnosis of hemorrhagic fevers using ARTMAP |
| dc.subject.keywords.none.fl_str_mv |
ARTMAP Dengue Differential diagnosis Hemorrhagic fever Leptospirosis Machine learning Malaria Neural networks Artificial intelligence Diseases Learning algorithms Learning systems Neural networks Patient monitoring Pattern recognition Statistical tests ARTMAP Dengue Differential diagnosis Hemorrhagic fever Leptospirosis Malaria Diagnosis |
| topic |
ARTMAP Dengue Differential diagnosis Hemorrhagic fever Leptospirosis Machine learning Malaria Neural networks Artificial intelligence Diseases Learning algorithms Learning systems Neural networks Patient monitoring Pattern recognition Statistical tests ARTMAP Dengue Differential diagnosis Hemorrhagic fever Leptospirosis Malaria Diagnosis |
| description |
The differential diagnosis of endemic hemorrhagic fevers in tropical countries is by no means an easy task for medical practitioners. Several diseases often overlap with others in terms of signs and symptoms, thus making this diagnosis a difficult, error-prone process. Machine Learning algorithms possess some useful qualities to tackle this kind of pattern recognition problems. In this paper, a neural-network-based approach to the differential diagnosis of Dengue Fever, Leptospirosis and Malaria, using the Adaptive Resonance Theory Map (ARTMAP) family is discussed. The use of an Artificial Immune System (CLONALG) led to the identification of a subset of symptoms that enhanced the performance of the classifiers considered. Training, validation and testing phases were conducted using a dataset consisting of medical charts from patients treated in the last 10 years at Napoleón Franco Pareja Children Hospital in Cartagena, Colombia. Results obtained on the test set are promising, and support the feasibility of this approach. © Springer-Verlag Berlin Heidelberg 2012. |
| publishDate |
2012 |
| dc.date.issued.none.fl_str_mv |
2012 |
| dc.date.accessioned.none.fl_str_mv |
2020-03-26T16:32:57Z |
| dc.date.available.none.fl_str_mv |
2020-03-26T16:32:57Z |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_c94f |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
| dc.type.hasversion.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.spa.none.fl_str_mv |
Conferencia |
| status_str |
publishedVersion |
| dc.identifier.citation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7637 LNAI, pp. 221-230 |
| dc.identifier.isbn.none.fl_str_mv |
9783642346538 |
| dc.identifier.issn.none.fl_str_mv |
03029743 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9102 |
| dc.identifier.doi.none.fl_str_mv |
10.1007/978-3-642-34654-5_23 |
| dc.identifier.instname.none.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.none.fl_str_mv |
Repositorio UTB |
| dc.identifier.orcid.none.fl_str_mv |
56341358400 55783129400 55782490400 |
| identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7637 LNAI, pp. 221-230 9783642346538 03029743 10.1007/978-3-642-34654-5_23 Universidad Tecnológica de Bolívar Repositorio UTB 56341358400 55783129400 55782490400 |
| url |
https://hdl.handle.net/20.500.12585/9102 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.conferenceplace.none.fl_str_mv |
Cartagena de Indias |
| dc.relation.conferencedate.none.fl_str_mv |
13 November 2012 through 16 November 2012 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_16ec |
| dc.rights.uri.none.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/restrictedAccess |
| dc.rights.cc.none.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_16ec |
| eu_rights_str_mv |
restrictedAccess |
| dc.format.medium.none.fl_str_mv |
Recurso electrónico |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Springer Verlag |
| publisher.none.fl_str_mv |
Springer Verlag |
| dc.source.none.fl_str_mv |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906654078&doi=10.1007%2f978-3-642-34654-5_23&partnerID=40&md5=28abcb7b52766f4d2de66fc2dcb420af Scopus2-s2.0-84906654078 |
| institution |
Universidad Tecnológica de Bolívar |
| dc.source.event.none.fl_str_mv |
13th Ibero-American Conference on Advancesin Artificial Intelligence, IBERAMIA 2012 |
| bitstream.url.fl_str_mv |
https://repositorio.utb.edu.co/bitstream/20.500.12585/9102/1/MiniProdInv.png |
| bitstream.checksum.fl_str_mv |
0cb0f101a8d16897fb46fc914d3d7043 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional UTB |
| repository.mail.fl_str_mv |
repositorioutb@utb.edu.co |
| _version_ |
1814021612482068480 |
| spelling |
2020-03-26T16:32:57Z2020-03-26T16:32:57Z2012Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7637 LNAI, pp. 221-230978364234653803029743https://hdl.handle.net/20.500.12585/910210.1007/978-3-642-34654-5_23Universidad Tecnológica de BolívarRepositorio UTB563413584005578312940055782490400The differential diagnosis of endemic hemorrhagic fevers in tropical countries is by no means an easy task for medical practitioners. Several diseases often overlap with others in terms of signs and symptoms, thus making this diagnosis a difficult, error-prone process. Machine Learning algorithms possess some useful qualities to tackle this kind of pattern recognition problems. In this paper, a neural-network-based approach to the differential diagnosis of Dengue Fever, Leptospirosis and Malaria, using the Adaptive Resonance Theory Map (ARTMAP) family is discussed. The use of an Artificial Immune System (CLONALG) led to the identification of a subset of symptoms that enhanced the performance of the classifiers considered. Training, validation and testing phases were conducted using a dataset consisting of medical charts from patients treated in the last 10 years at Napoleón Franco Pareja Children Hospital in Cartagena, Colombia. Results obtained on the test set are promising, and support the feasibility of this approach. © Springer-Verlag Berlin Heidelberg 2012.Sociedad Colombiana de Computacion (SCo2);Universidad de Caldas;Universidad Nacional de Colombia;Universidad Tecnologica de Bolivar en Cartagena;Universidad Tecnologica de PereiraRecurso electrónicoapplication/pdfengSpringer Verlaghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84906654078&doi=10.1007%2f978-3-642-34654-5_23&partnerID=40&md5=28abcb7b52766f4d2de66fc2dcb420afScopus2-s2.0-8490665407813th Ibero-American Conference on Advancesin Artificial Intelligence, IBERAMIA 2012Differential diagnosis of hemorrhagic fevers using ARTMAPinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fARTMAPDengueDifferential diagnosisHemorrhagic feverLeptospirosisMachine learningMalariaNeural networksArtificial intelligenceDiseasesLearning algorithmsLearning systemsNeural networksPatient monitoringPattern recognitionStatistical testsARTMAPDengueDifferential diagnosisHemorrhagic feverLeptospirosisMalariaDiagnosisCartagena de Indias13 November 2012 through 16 November 2012Caicedo W.Quintana Álvarez, Moisés RamónPinzón H.Brown, M., Vickers, I., Salas, R., Smikle, M., Leptospirosis in suspected cases of dengue in jamaica 2002-2007 (2010) Tropical Doctor, 40 (2), pp. 92-94Burnet, F., (1959) The Clonal Selection Theory of Acquired Immunity, , Vanderbilt University PressCarpenter, G., Default artmap (2003) International Joint Conference on Neural Networks (IJCNN 2003), 2, pp. 1396-1401. , IEEECarpenter, G., Grossberg, S., A massively parallel architecture for a self-organizing neural pattern recognition m achine (1987) Computer Vision, Graphics, and Image Processing, 37 (1), pp. 54-115Carpenter, G., Grossberg, S., ART 2-self-organization of stable category recognition codes for analog input patterns (1987) Applied Optics, 26 (23), pp. 4919-4930Carpenter, G., Grossberg, S., ART3-Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures (1990) Neural Networks, 3 (2), pp. 129-152Carpenter, G., Grossberg, S., Markuzon, N., Reynolds, J., Rosen, D., Fuzzy artmap-A neural network architecture for incremental supervised learning of analog multid imensional maps (1992) IEEE Transactions on Neural Networks, 3 (5), pp. 698-713Carpenter, G., Grossberg, S., Rosen, D., Fuzzy art-fast stable learning and categorization of analog patterns b y an adaptive resonance system (1991) Neural Networks, 4 (6), pp. 759-771Carpenter, G., Markuzon, N., Artmap-ic and medical diagnosis-instance counting and inconsistent cases (1998) Neural Networks, 11 (2), pp. 323-336Chadwick, D., Arch, B., Wilder-Smith, A., Paton, N., Distinguishing dengue fever from other infections on the basis of simple clinical and laboratory features-application of logistic regression analysis (2006) Journal of Clinical Virology, 35 (2), pp. 147-153De Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251Downs, J., Harrison, R., Kennedy, R., Cross, S., Application of the fuzzy artmap neural network model to medical pattern classification tasks (1996) Artificial Intelligence in Medicine, 8 (4), pp. 403-428Ellis, T., Imrie, A., Katz, A., Effler, P., Underrecognition of leptospirosis during a dengue fever outbreak in Hawaii 2001-2002 (2008) Vector-Borne and Zoonotic Diseases, 8 (4), pp. 541-547Goodman, P., Kaburlasos, V., Egbert, D., Carpenter, G., Grossberg, S., Reynolds, J., Rosen, D., Hartz, A., Fuzzy artmap neural network compared to linear discriminant analysis prediction of the length of hospital stay in patients with pneumonia (1992) IEEE Intl. Conf. on Systems Man and Cybernetics (ICSMC 1992), 1, pp. 748-753Halstead, S.E., (2008) Dengue, Tropical Medicine-Science and Practice, , (ed.) Imperial College PressKgrostad, D., Plasmodium species (malaria) (2000) Principles and Practice of Infectious Diseases, pp. 2818-2831. , Mandell, G., Bennet, J.E., Dolin, R. (eds.) Churchill LivingstoneKohonen, T., Self-organized formation of topologically correct feature maps (1982) Biological Cybernetics, 43 (1), pp. 59-69Levett, P., Branch, S., Edwards, C., Detection of dengue infection in patients investigated for leptospirosis in barbados (2000) The American Journal of Tropical Medicine and Hygiene, 62 (1), pp. 112-114Libraty, D.H., Myint, K.S.A., Murray, C.K., Gibbons, R.V., Mammen, M.P., Endy, T.P., Li, W., Ennis, F.A., A comparative study of leptospirosis and dengue in thai children (2007) PLoS Neglected Tropical Diseases, 1 (3), pp. e111Markuzon, N., Gaehde, S., Ash, A., Carpenter, G., Moskowitz, M., Predicting risk of A N adverse event in complex medical data sets using fuzzy artmap network (1994) Technical Report Series, pp. 93-96. , Artificial Intelligence in Medicine-Interpreting Clinical DataPotts, J., Rothman, A., Clinic al and laboratory features that distinguish dengue from other febrile illnesses in endemic populations (2008) Tropical Medicine & International Health, 13 (11), pp. 1328-1340Tappero, J., Ashford, D., Perkins, B., Leptospira species (leptospirosis) (2000) Principles and Practice of Infectious Diseases, pp. 2495-2501. , Mandell, G., Bennet, J.E., Dolin, R. (eds.) Churchill LivingstoneTsai, T., Flaviviruses (2000) Principles and Practice of Infectious Diseases, pp. 1714-1735. , Mandell, G., Bennet, J.E., Dolin, R. (eds.) Churchill LivingstoneYang, Y., An evaluation of statis tical approaches to text categorization (1999) Information Retrieval, 1 (1), pp. 69-90http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9102/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9102oai:repositorio.utb.edu.co:20.500.12585/91022023-04-24 09:35:09.739Repositorio Institucional UTBrepositorioutb@utb.edu.co |