Authors:Rama Thelagathoti andHesham Ali
Affiliation:College of Information Science and Technology, University of Nebraska Omaha, Omaha, NE 68182, U.S.A.
Keyword(s):Graph Models, Correlation Networks, Depression Studies, Mobility Data, Objective Diagnosis, Severity Assessment.
Abstract:Depression is a serious behavioural disorder that can affect the quality of life. Timely diagnosis and accurate estimation of severity are critical in supporting depression studies and starting early interventional treatment. In this study, we introduce two major contributions. First, we propose a novel computational model that can utilize non-invasive mobility data to recognize individuals suffering from depression disorders. Second, we introduce a new objective index, the Depression Severity Score Index (DSS), which can approximate the seriousness or the degree of severity of depression. The proposed approach is a data-driven model that is built on the mobility data collected from 55 subjects using wearable sensors. In the first step in our proposed approach, a graph model that represents the underlying correlation network is constructed by measuring the pair-wise correlation values between each pair of individuals. Then, we obtain the depression severity index of each subject by utilizing graph properties of the constructed network such as Intra and inter-cluster edges. Our obtained results show that the obtained correlation network model has the potential to identify participants diagnosed with depression from the control group. Moreover, the proposed Depression Severity Score (DSS) has a higher likelihood than the clinical depression score in correctly measuring the depression severity level.(More)
Depression is a serious behavioural disorder that can affect the quality of life. Timely diagnosis and accurate estimation of severity are critical in supporting depression studies and starting early interventional treatment. In this study, we introduce two major contributions. First, we propose a novel computational model that can utilize non-invasive mobility data to recognize individuals suffering from depression disorders. Second, we introduce a new objective index, the Depression Severity Score Index (DSS), which can approximate the seriousness or the degree of severity of depression. The proposed approach is a data-driven model that is built on the mobility data collected from 55 subjects using wearable sensors. In the first step in our proposed approach, a graph model that represents the underlying correlation network is constructed by measuring the pair-wise correlation values between each pair of individuals. Then, we obtain the depression severity index of each subject by utilizing graph properties of the constructed network such as Intra and inter-cluster edges. Our obtained results show that the obtained correlation network model has the potential to identify participants diagnosed with depression from the control group. Moreover, the proposed Depression Severity Score (DSS) has a higher likelihood than the clinical depression score in correctly measuring the depression severity level.