Depression is highly prevalent, both in the US and worldwide. Among US adults, the estimated 12-month and lifetime prevalence rates are 8.3% and 19.2%, respectively. The World Health Organization considers major depressive disorder (MDD) as the third-highest cause of disease burden worldwide, and the highest cause of disease burden in the developed world. However, despite its prevalence and burden, depression remains significantly under-recognized and under-treated in all practice settings, including managed care where less than one third of adults with depression obtain appropriate professional treatment. Denial of illness and stigma are two primary barriers to proper identification and treatment of depression. Many individuals with depression are ashamed to seek out a mental health professional and consider depression a sign of personal weakness. In particular, self-stigma has been associated to affect adherence to psychiatric services, hope and quality of life negatively, and also poses as a barrier for social integration. Further, since self-stigma can exist without actual stigma from the public, and is more hidden and inside, it seems to be the worst form of stigma against people with depression and can directly affect the patients' over all well-being. Studies suggest that early recognition and treatment of depressive behavior and symptoms can improve social function, increase productivity, and decrease absenteeism in the workplace. However, recognition of depression, particularly in early stages, is still challenging. To address this problem, in this proposal we plan to develop effective methods for detection of depressive behavior, not only at an individual-level, but also at a community-level. The latter is highly pertinent because depression is significantly influenced by variations in social determinants and socio- ecological factors. In particular, we will leverage robust and longitudinal electronic health record (EHR) systems at Mayo Clinic and private insurance (UnitedHealthCare/Optum Labs) reimbursement and claims data along with online social media data from Twitter and PatientsLikeMe as well as geo-coded neighborhood and environmental data to develop a big data platform for identifying combinations of online socio-behavioral factors and neighborhood environmental conditions to enable innovative ways for detection of depressive behavior within communities and identify patterns and changes in health care utilization for depression across different communities and geographies within U.S.
Depression is one of the most common mental disorders in the U.S. and is the leading cause of disability affecting millions of Americans every year. Successful early identification and treatment of depression can lead to many other positive health and behavioral outcomes across the lifespan. This proposal will apply 'big data' techniques and methods for identifying combinations of online socio-behavioral factors and neighborhood environmental conditions that can enable detection of depressive behavior in communities and studying access and utilization of healthcare services.
| Kim, Min-Hyung; Banerjee, Samprit; Zhao, Yize et al. (2018) Association networks in a matched case-control design - Co-occurrence patterns of preexisting chronic medical conditions in patients with major depression versus their matched controls. J Biomed Inform 87:88-95 |
| Gunaratna, Kalpa; Yazdavar, Amir Hossein; Thirunarayan, Krishnaprasad et al. (2017) Relatedness-based Multi-Entity Summarization. IJCAI (U S) 2017:1060-1066 |
| Yazdavar, Amir Hossein; Al-Olimat, Hussein S; Ebrahimi, Monireh et al. (2017) Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media. Proc IEEE ACM Int Conf Adv Soc Netw Anal Min 2017:1191-1198 |
| Sheth, Amit; Jaimini, Utkarshani; Thirunarayan, Krishnaprasad et al. (2017) Augmented Personalized Health: How Smart Data with IoTs and AI is about to Change Healthcare. RTSI 2017: |
| Hong, Na; Pathak, Jyotishman; Chute, Christopher G et al. (2016) Developing a modular architecture for creation of rule-based clinical diagnostic criteria. BioData Min 9:33 |
| Ryu, Euijung; Chamberlain, Alanna M; Pendegraft, Richard S et al. (2016) Quantifying the impact of chronic conditions on a diagnosis of major depressive disorder in adults: a cohort study using linked electronic medical records. BMC Psychiatry 16:114 |
| Taslimitehrani, Vahid; Dong, Guozhu; Pereira, Naveen L et al. (2016) Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function. J Biomed Inform 60:260-9 |
| Lamy, Francois R; Daniulaityte, Raminta; Sheth, Amit et al. (2016) ""Those edibles hit hard"": Exploration of Twitter data on cannabis edibles in the U.S. Drug Alcohol Depend 164:64-70 |
| Wijeratne, Sanjaya; Balasuriya, Lakshika; Sheth, Amit et al. (2016) EmojiNet: Building a Machine Readable Sense Inventory for Emoji. Proc Int Workshop Soc Inform 10046:527-541 |
| Balasuriya, Lakshika; Wijeratne, Sanjaya; Doran, Derek et al. (2016) Finding Street Gang Members on Twitter. Proc IEEE ACM Int Conf Adv Soc Netw Anal Min 206:685-692 |
Showing the most recent 10 out of 14 publications
