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dc.contributor.author
Acosta, Julián  
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Grimaldi, Francisco  
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Dorr, Francisco  
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Varela, Francisco  
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Alessandro, Lucas  
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Goicochea, María Teresa  
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Fernandez Slezak, Diego  
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Farez, Mauricio Franco  
dc.date.available
2022-07-27T19:13:40Z  
dc.date.issued
2018  
dc.identifier.citation
Accuracy and safety of an artificial intelligent system for nonacute headache diagnosis; 70th Annual American Academy of Neurology Annual Meeting; Los Ángeles; Estados Unidos; 2018; 1-6  
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0028-3878  
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http://hdl.handle.net/11336/163320  
dc.description.abstract
Objective: Evaluate accuracy and safety of an artificial intelligent (AI) system for nonacute headache diagnosis. Background: Headache is the main cause of neurologic consultation, entailing high cost in healthcare systems and a great impact in quality of life of patients suffering from it. Moreover, the access to qualified specialists and appropriate treatment is not ensured, especially in areas with low number of neurologist per capita. We hypothesize that and AI-system could assist in the diagnosis of headaches with a precision and safety comparable to a specialist. Design/Methods: We reviewed a database of 580 clinical records of patients with headache as chief complaint. Clinical records were processed with Latent Semantic Analysis (LSA) and a Support Vector Machine (SVM) model was trained. The definite diagnosis was the one given by the specialist at the consultation. We compared the SVM model performance at classifying the headache as primary versus secondary with two general neurologist. Finally, we used an interactive headache questionnaire filled by patients previous to the consultation and classified the headache with an automatic ICHD criteria system supplemented with a machine-learning model, comparing that diagnosis to the one given by neurologists. All the development and analysis was done using Python. Results: The SVM model trained after “reading” clinical records with LSA had a better performance in the diagnosis of secondary headache (sensitivity=90.2%; specificity=93%) in comparison with other neurologists (sensitivity=82%; specificity=85%). A correct headache diagnosis was achieved in 89–94% of the cases when ICHD criteria was combined with several machine-learning models. Conclusions: AI has a great potential for its application in headache diagnosis. Advancements in this field would both improve the accessibility to quality healthcare and optimize the time spent by health professionals. Disclosure: Dr. Acosta has nothing to disclose. Dr. Grimaldi has nothing to disclose. Dr. Dorr has nothing to disclose. Dr. Varela has nothing to disclose. Dr. Alessandro has nothing to disclose. Dr. Goicochea has nothing to disclose. Dr. Fernández Slezak has nothing to disclose. Dr. Farez has nothing to disclose.  
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application/pdf  
dc.language.iso
eng  
dc.publisher
Wolters Kluwer  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
NONACUTE  
dc.subject
HEADACHE  
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DIAGNOSIS  
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ARTIFICIAL INTELLIGENT SYSTEM  
dc.subject.classification
Ciencias de la Computación  
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Ciencias de la Computación e Información  
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CIENCIAS NATURALES Y EXACTAS  
dc.title
Accuracy and safety of an artificial intelligent system for nonacute headache diagnosis  
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info:eu-repo/semantics/publishedVersion  
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info:eu-repo/semantics/conferenceObject  
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info:ar-repo/semantics/documento de conferencia  
dc.date.updated
2022-07-20T16:01:22Z  
dc.identifier.eissn
1526-632X  
dc.journal.volume
90  
dc.journal.number
15  
dc.journal.pagination
1-6  
dc.journal.pais
Países Bajos  
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Alphen aan den Rijn  
dc.description.fil
Fil: Acosta, Julián. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina  
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Fil: Grimaldi, Francisco. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina  
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Fil: Dorr, Francisco. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina  
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Fil: Varela, Francisco. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina  
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Fil: Alessandro, Lucas. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina  
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Fil: Goicochea, María Teresa. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina  
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Fil: Fernandez Slezak, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina  
dc.description.fil
Fil: Farez, Mauricio Franco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://n.neurology.org/content/90/15_Supplement/P3.127.abstract  
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dc.coverage
Internacional  
dc.type.subtype
Congreso  
dc.description.nombreEvento
70th Annual American Academy of Neurology Annual Meeting  
dc.date.evento
2018-04-21  
dc.description.ciudadEvento
Los Ángeles  
dc.description.paisEvento
Estados Unidos  
dc.type.publicacion
Journal  
dc.description.institucionOrganizadora
American Academy of Neurology  
dc.source.revista
Neurology  
dc.date.eventoHasta
2018-04-27  
dc.type
Congreso