Research Projects

In this proposal, the PIs develop novel theoretical foundations and scalable algorithms for tensor decomposition and apply them to some major Big Data tasks such as noise removal, data imputation, information integration and dimension reduction. To evaluate the performance of the proposed tensor methods, they will be applied to early detection of sepsis, which represents a spectrum of complex problems in medicine and science.

The proposed system will accurately predict or detect the onset of severe cardiac events (e.g., hemodynamic instability) prior to complications experienced by the driver in order to ensure driver safety.

In this project, we aim to develop an automated decision-making system that can identify patients eligible for an HT/MCS that would facilitate primary care physicians or general cardiologists referring those patients for consideration of advanced therapies.

Among other image modalities used for TBI assessment, a number of recent studies reported that hand-held optic nerve ultrasound (ONUS) may help identify elevated intracranial pressure (ICP). In the largest study of ICP prediction using ONUS to date, we found that optic nerve sheath diameter can accurately predict ICP > 25 mmHg in a mixed population of patients in a neurosurgical ICU. Our preliminary work has demonstrated the feasibility of fully automated algorithms to measure optic nerve sheath diameter. In this project we will further develop these algorithms to predict ICP from measuring the optic nerve sheath diameter.

Trauma is the leading cause of death among Americans under 44. The mortality rate for patients with pelvic injuries is increased by the risk of further complications, especially severe hemorrhage. However, even a single trauma patient generates large volumes of information, including vital signs, injury severity scores, demographic details, lab reports, and in particular, complex medical images such as CT scans and X-rays—all of which impact diagnosis and treatment, as well as costs. A computer-assisted decision support system (DSS) capable of rapidly analyzing large volumes of patient information to generate accurate treatment recommendations and outcome predictions has the potential to improve both patient care/survival and resource utilization. The proposed Decision Support System (DSS) technology is envisioned to significantly improve pelvic/abdominal trauma decision-making using facilitated and prompt analysis of complex and heterogeneous patient medical data.

The current project will extend the algorithms of our DDS technology to:

1. Integrate our algorithms and methods for analysis of data for pelvic/abdominal injuries with those of TBI such that the resulting DDS software can be used in polytrauma cases.
2. Further develop the planned software implementation of our technology as a user-friendly software tool to be used in the clinical setting.

Hypertension is the number one cause of racial disparities in mortality in the U.S. Understanding the mechanisms by which race is linked with the cardiovascular system is key for potentially reducing race disparities in hypertension and hypertension related mortality. The present study is guided by existing theories of racial health disparities, which suggest that racial health disparities are due to variations in long-term exposure to stress that are moderated by social relations and age.

This project has three aims:

1. Test links between long-term stress exposure and short-term stress reactivity among Eurpoean American (EA) and AA adults.
2. Examine age differences in long-term stress exposure and short-term reactivity by race.
3. Determine how long-term social relationships moderate individual differences in stress exposure and reactivity.