title: Collective intelligence in medical diagnosis systems: A case study.
abstract: Diagnosing a patient's condition is one of the most important and challenging tasks in medicine. We present a study of the application of collective intelligence in medical diagnosis by applying consensus methods. We compared the accuracy obtained with this method against the diagnostics accuracy reached through the knowledge of a single expert. We used the ontological structures of ten diseases. Two knowledge bases were created by placing five diseases into each knowledge base. We conducted two experiments, one with an empty knowledge base and the other with a populated knowledge base. For both experiments, five experts added and/or eliminated signs/symptoms and diagnostic tests for each disease. After this process, the individual knowledge bases were built based on the output of the consensus methods. In order to perform the evaluation, we compared the number of items for each disease in the agreed knowledge bases against the number of items in the GS (Gold Standard). We identified that, while the number of items in each knowledge base is higher, the consensus level is lower. In all cases, the lowest level of agreement (20%) exceeded the number of signs that are in the GS. In addition, when all experts agreed, the number of items decreased. The use of collective intelligence can be used to increase the consensus of physicians. This is because, by using consensus, physicians can gather more information and knowledge than when obtaining information and knowledge from knowledge bases fed or populated from the knowledge found in the literature, and, at the same time, they can keep updated and collaborate dynamically.
authors.affiliation: Information Technology and Communication Division, Universidad Tecnológica Metropolitana, Circuito Colonias Sur No 404, 97279 Mérida, México. Electronic address: gandhi.hernandez@utmetropolitana.edu.mx.
authors.forename: Gandhi S
authors.initials: GS
authors.lastname: Hernández-Chan
authors.affiliation: Information Technology and Communication Division, Universidad Tecnológica Metropolitana, Circuito Colonias Sur No 404, 97279 Mérida, México. Electronic address: eduardo.ceh@utmetropolitana.edu.mx.
authors.forename: Edgar Eduardo
authors.initials: EE
authors.lastname: Ceh-Varela
authors.affiliation: Computer Science Department, Universidad Carlos III de Madrid, Av. Universidad 30, Leganés 28911, Madrid, Spain. Electronic address: joseluis.s.cervantes@alumnos.uc3m.es.
authors.forename: Jose L
authors.initials: JL
authors.lastname: Sanchez-Cervantes
authors.affiliation: Computer Science Department, Instituto Tecnológico de Mérida, Av. Tecnológico km. 4.5 S/N C.P. 97118, Mexico. Electronic address: mvillanueva@itmerida.mx.
authors.forename: Marisol
authors.initials: M
authors.lastname: Villanueva-Escalante
authors.affiliation: ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid, Campus de Montegancedo, Boadilla del Monte, 28660 Madrid, Spain. Electronic address: alejandro.rodriguezg@upm.es.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Rodríguez-González
authors.affiliation: Computer Science Department, Universidad Carlos III de Madrid, Av. Universidad 30, Leganés 28911, Madrid, Spain. Electronic address: yuliana.perez@alumnos.uc3m.es.
authors.forename: Yuliana
authors.initials: Y
authors.lastname: Pérez-Gallardo
mesh_terms: D064229:Biological Ontologies; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D051188:Knowledge Bases
keywords: Collective intelligence; Consensus methods; Diagnosis; Diagnosis Decision Support System; Semantics