Title :
A comorbidity-based recommendation engine for disease prediction
Author :
Folino, Francesco ; Pizzuti, Clara
Author_Institution :
Inst. for High Performance Comput. & Networking (ICAR), Italian Nat. Res. Council (CNR), Rende, Italy
Abstract :
A recommendation engine for disease prediction that combines clustering and association analysis techniques is proposed. The system produces local prediction models, specialized on subgroups of similar patients by using the past patient medical history, to determine the set of possible illnesses an individual could develop. Each model is generated by using the set of frequent diseases that contemporarily appear in the same patient. The illnesses a patient could likely be affected in the future are obtained by considering the items induced by high confidence rules generated by the frequent diseases. Experimental results show that the proposed approach is a feasible way to diagnose diseases.
Keywords :
diseases; medical information systems; patient diagnosis; pattern clustering; recommender systems; association analysis techniques; clustering analysis techniques; comorbidity based recommendation engine; disease diagnosis; disease prediction; high confidence rules; patient medical history; Clustering algorithms; Computational modeling; Diseases; History; Itemsets; Medical diagnostic imaging; Predictive models;
Conference_Titel :
Computer-Based Medical Systems (CBMS), 2010 IEEE 23rd International Symposium on
Conference_Location :
Perth, WA
Print_ISBN :
978-1-4244-9167-4
DOI :
10.1109/CBMS.2010.6042664
