CHOT Scholar Projects focus on improving healthcare delivery and quality of care using research and tools that exist across various disciplines. These projects involve students working under the guidance of faculty and/or industry advisors. The projects can often form a key component of their masters’ thesis or doctoral dissertation.
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CHOT Scholar Projects 2019-2020
- Virtual Reality For Health Systems Simulation
Virtual Reality (VR) is becoming an essential part of modern education and training. In healthcare, VR can be used to improve decision making and help patients to better connect with reality, cope with pain, and overcome anxiety and depression. This project will integrate sensing technology (i.e., eye and motion tracking) and VR simulation of healthcare environments to improve clinical judgment and treat individuals with mental disorders by improving their metacognitive skills. VR simulations will be compared to traditional patient simulations and mental health screening tools. Researchers will develop analytical models to improve clinical judgment and predict the risk of mental illness.
- Privacy-Preserving Data Analytics for Smart and Interconnected Health Systems
Internet of Things (IoT) transforms traditional health systems into new data-rich environments. This provides an unprecedented opportunity to develop new analytical methods and tools to realize a new paradigm of smart and interconnected health systems. However, data breach and malicious attacks are increasingly seen in healthcare, which brings unexpected disruptions to routine operations and cause the loss of billions of dollars. As healthcare systems are critical to improving the wellbeing of our society, there is an urgent need to protect privacy information of patients, and minimize the risk of model inversion attacks.
- Data-enabled Predictive Modeling and Intervention Optimization of Breast Cancer
Breast cancer is a prevailing problem that decreases the quality of patients’ lives, creates high burdens on health systems, and impacts the well-being of society. Advanced sensing provides an unprecedented opportunity to revolutionize cancer care delivery and decrease associated costs. However, disparity, uncertainty, and incompleteness of the sensing data, as well as the lack of tailored decision-making models, are the major challenges that prevent practitioners from gaining substantial information to improve their efficiency. We continue our healthcare innovation by developing advanced analytical models that address the challenges of data and provide in-depth knowledge for decision making from heterogeneous cancer recordings systems. We will investigate on survival model to decipher important factors that play a role in the recurrence of cancer. Also, we will introduce novel analytical models to estimate risk reduction strategies and develop an evidence-based decision support system in breast cancer. The proposed models can also be generally applicable to a variety of medical domains that entail data imputation, analytical solutions, and decision making.
- Prediction and Intervention Models for Combating the Opioid Epidemic
Each day, more than 115 Americans die as a result of overdosing on opioids. The objective of this project is to develop a prediction, prevention, and intervention model that seeks to combat the opioid epidemic. This project seeks to connect the multiple facets of the opioid epidemic into a unifying decision support system that not only predicts the risks of opioid addiction, but also proposes reliable, safe, and effective prevention and mitigation strategies. Aim 1: Develop statistical models to predict the effectiveness of treatment services and key factors pertinent to opioid mortality (social-economic data, demographic information, prescription, and insurance information, etc.). Aim 2: Develop stochastic differential equation (SDE) models to simulate the opioid trafficking flows and key factors that control the supply chain (e.g., law enforcement, tax tariffs, prescription, insurance policy). Aim 3: Optimizing the decision policy to combat the opioid crisis.
- Optimal Physician-Patient Matching: A Novel Methodology for Health Care Market Economics
The internet has promoted a sharing economy, which has ushered in the need to develop a new generation of health care service platforms. One critical problem is to improve the matching of customers with services. While physicians have limited capacities and patients arrive randomly, optimal recourse allocation in such a dynamic environment is challenging. Very little has been done to investigate online matching algorithms for the optimization of recourse allocation in physician-patient matching, and the aim is to establish a new sharing-economy framework for smart health care service systems.
To view past CHOT Scholar Projects, visit here.