A prediction model that exploits the past medical patient history to determine the risk of individuals to develop future diseases is proposed. The 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. Furthermore, a phenotypic comorbidity network is built and its structural properties are studied in order to better understand the connections between illnesses. Experimental results show that the proposed approach is a promising way for assessing disease risk.
A Comorbidity Network Approach to Predict Disease Risk
Francesco Paolo Folino;Clara Pizzuti;
2010
Abstract
A prediction model that exploits the past medical patient history to determine the risk of individuals to develop future diseases is proposed. The 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. Furthermore, a phenotypic comorbidity network is built and its structural properties are studied in order to better understand the connections between illnesses. Experimental results show that the proposed approach is a promising way for assessing disease risk.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
