Research

As a PhD student, I have been working in two independent projects, namely Wellbuilt-for-Wellbeing and Knowledge discovery through disease networks. The first project is a multi-disciplinary project with an overarching goal of identifying environmental factors affecting individual wellbeing in the workplace. In the second project, I study the impact of the simultaneous occurrence of two or more diseases on patient health outcomes. Through the two projects, I got the opportunity of analyzing health and wellbeing from a clinical perspective as well as a non-clinical perspective; a unique experience that was instrumental in shaping my research trajectory.

My current research outputs can be placed into three categories: (a) Preventive care with focus on disease occurrence and co-occurrence in patient visits, (b) Digital health application of environment-wellbeing modeling at workplace, (c) Data analytics methods

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Following are the articles published, under-review and in working status:

Refereed Journals

Srinivasan K., Currim F., Ram S. “Predicting High Cost Patients at Point of Admission using Network Science”, Journal of Biomedical Health Informatics, Dec 2017 (early access).

Lindberg C., Srinivasan K., et al. “Effects of office workstation type on physical activity and stress”, Occupational and Environmental Medicine, Jul 2018.

Ghahramani A., Pantelic J., Lindberg C., Mehl M., Srinivasan K., et al. “Learning occupants’ workplace interactions from wearable and stationary ambient sensing systems”, Applied Energy, Nov 2018.

Manuscripts Under Review

Lee H., Razjouyan J., Nyugen H., Lindberg C., Srinivasan K., et al. “Sensor-Based Sleep Quality Index (SB-SQI): a New Metric to Examine the Association of Office Workstation Type on Stress and Sleep”, under review with Sensors, Jul 2018.

Razjouyan J., Lee H., Nyugen H., Lindberg C., Srinivasan K., et al. “Wellbuilt for wellbeing: Why Controlling Relative Humidity Matters for Our Health?”, under review with New England Journal of Medicine, Jul 2018.

Working Papers

Srinivasan K., Currim F., Ram S. et al. “Statistical Modeling Methods for Wearable Data Analytics: Application in Workplace Sound-Wellbeing Modeling”, to be submitted to Information Systems Research.

Srinivasan K., Currim F., Ram S. “Analyzing Incomplete Data with Block-wise Missing Patterns”. to be submitted to Information Systems Research.

Work in Progress

Srinivasan K., Currim F., Ram S. “Predicting diseases using wearable sensors”, Work-in-progress (Data collection).

Refereed Conference Proceedings

Srinivasan K., Currim F., Ram S. et al. “Using digital health wearable devices to understand the relationship between sound levels and wellbeing: A segmented mixed-effects regression approach”. Proceedings of the 17th Annual Workshop on Information Technology, 2017.

Srinivasan K., Currim F., Ram S. et al. “A regularization approach for identifying cumulative lagged effects in smart health applications”. Proceedings of the 7th International Conference on Digital Health, 2017.

Srinivasan K., Currim F., Ram S. et al. “Feature importance and prediction modeling for multi-source healthcare data with missing values”. Proceedings of the 6th International Conference on Digital Health, 2016 (Best paper award).

Srinivasan K., Ram S. “Indoor environmental effects on individual wellbeing”. Proceedings of the 6th International Conference on Digital Health, 2016 (Extended Abstract).

Raturi V., Srinivasan K., Narulkar G., Chandrashekharaiah A., and Gupta A. “Analyzing inter-modal competition between high speed rail and conventional transport systems: A game theoretic approach”. Proceedings of the Second Conference of Transportation Research Group of India, 2013.

White papers

Ram S., Srinivasan K., Chagarlamudi S. “Analysis of Chronic Disease Related Patient Visits in Arizona Hospitals”, Making Action Possible dashboard report, Nov 2018.

Selected Media mentions of Research

Illustrations

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Disease co-occurrence network, community detection and diseases/conditions in individual communities

Guest lecture titled “Predicting High Cost Patients at Point of Admission using Network Science”