Collective intelligence in medical diagnosis systems: A case study

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• 作者：Gandhi S. Herná ; ndez-Chan^a ; ^{gandhi.hernandez@utmetropolitana.edu.mx" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Edgar Eduardo Ceh-Varela^a ; ^{eduardo.ceh@utmetropolitana.edu.mx" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jose L. Sanchez-Cervantes^b ; ^{joseluis.s.cervantes@alumnos.uc3m.es" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Marisol Villanueva-Escalante^c ; ^{mvillanueva@itmerida.mx" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Alejandro Rodrí ; guez-Gonzá ; lez^d ; ^{alejandro.rodriguezg@upm.es" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yuliana Pé ; rez-Gallardo^b ; ^{yuliana.perez@alumnos.uc3m.es" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Diagnosis Decision Support System ; Collective intelligence ; Consensus methods ; Diagnosis ; Semantics
• 刊名：Computers in Biology and Medicine
• 出版年：2016
• 出版时间：1 July 2016
• 年：2016
• 卷：74
• 期：Complete
• 页码：45-53
• 全文大小：1468 K

文摘

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.