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Penn State National Science Foundation Center for Health Organization Transformation
(Penn State CHOT) - An Industry-University Cooperative Research Center
July 2019
CHOT Leadership

New Director for the Penn State Center for Health Organization Transformation
Beginning August 15, 2019, former Penn State CHOT Director Dr. Conrad Tucker will begin his faculty position in the Department of Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in the School of Computer Science's Machine Learning Department. We would like to thank Dr. Tucker for his service and leadership to grow Penn State CHOT in the past three years. 
Dr. Hui Yang, Harold and Inge Marcus Career Associate Professor of Industrial and Manufacturing Engineering at Penn State, will serve as the new director. Dr. Yang's research interests focus on sensor-based modeling and analysis of complex health systems for process monitoring, process control, system diagnostics, condition prognostics, quality improvement, and performance optimization.
His research is supported by the National Science Foundation (NSF), Lockheed Martin, the NSF center for e-Design, the Susan G. Komen Foundation, the NSF Center for Healthcare Organization Transformation, the Penn State Institute for CyberScience, James A. Haley Veterans' Hospital, and Florida Health James and Esther King Biomedical research program. Dr. Yang is also a NSF CAREER Award recipient for "Sensor-based Modeling and Control of Nonlinear Dynamics in Complex Systems for Quality Improvements in Manufacturing and Healthcare."
Dr. Yang was the president (2017-2018) of the Institute of Industrial and Systems Engineers (IISE) Data Analytics and Information Systems Society, the president (2015-2016) of INFORMS Quality, Statistics and Reliability (QSR) society, and the program chair of 2016 Industrial and Systems Engineering Research Conference (ISERC). He is also a department editor for IISE Transactions on Healthcare Systems Engineering, an associate editor for IISE Transactions, IEEE Journal of Biomedical and Health Informatics (JBHI), IEEE Transactions on Automation Science and Engineering (TASE), an associate editor for Proceedings of 2019 IEEE Engineering in Medicine and Biology Conference, and Proceedings of 2019 IEEE Conference on Biomedical and Health Informatics.   
"It is with great pleasure that I will serve as the new director of CHOT," said Yang. "I will make every effort to march along the path of my illustrious predecessors, Dr. Conrad Tucker and Dr. Harriet Nembhard, towards building continued success of NSF CHOT site at Penn State, as well as NSF CHOT at large. I would like to thank Dr. Conrad Tucker, Dr. Chris DeFlitch, and Beth Colledge, as they worked closely with me on the transition plan for CHOT. I look forward to working with CHOT industry members, collaborators, and funding agencies to grow our organization and to bring new transformations to health systems while improving the health of our society."
Please contact Dr. Hui Yang,, for suggestions and new ideas on Penn State CHOT.  
Recent activities

Dr. Yang visited the James A. Haley Veterans' Hospital to discuss data analytics for smart health in intensive care units 
Intensive care units (ICU) house the most seriously ill patients in the hospital. The annual admission to an ICU in the U.S. is more than 5.7 million with a projected cost of $81.7 billion. To cope with disease complexity and enhance information visibility, advanced sensing is increasingly important and leads to data-rich environments in an ICU. For example, modern ICUs require the monitoring of heterogeneous types of clinical variables including laboratory tests (e.g.,urea, creatinine, sodium, and potassium), heart rate and rhythm, blood pressure, respiratory rate, blood-oxygen saturation and more.
Healthcare practitioners are facing big data every day; however, big data is underutilized in improving the quality of healthcare delivery. Physicians often make medical decisions based on the most recent data while overlooking historical data and hidden relationships among the heterogeneous set of clinical variables. In addition, as human discretion dominates the collection of clinical data, missing data is a general problem in the ICU environment. There is an urgent need to go beyond current clinical practices and further develop advanced data-driven methods to enable the delivery of smart healthcare in ICUs.  
The team aims to develop new data-driven methods to handle uncertainty and incompleteness in big ICU data to predict the dynamics of the patient recovery process. Such analytical methods are particularly timely in helping healthcare practitioners leverage the increasing availability of big data to achieve a substantial boost in smart ICU health management.

Dr. Hui Yang delivered a plenary talk on " Internet of Things and Data-enabled Innovations for Smart Service Systems" at the 2019 INFORMS Conference on Service Science
Rapid advances of sensor networks and 5G communication technology bring the new wave of Internet of Health Things (IoHT). The IoHT deploys a multitude of sensors for smart and connected health monitoring and management of human subjects and ambient conditions. Real-time IoHT sensing gives rise to "big data"; however, realizing the full potential of IoHT data depends on the development of new sensor-based methodologies for data-enabled healthcare innovations. In this talk, Dr. Yang presented a review of the new generation of smart and interconnected health systems that will "enable all information about the healthcare process to be available whenever it is needed, wherever it is needed, and in an easily comprehensible form across the health system and among interconnected health systems." Specifically, this talk focused on multi-level network modeling and analysis of the large-scale IoHT. The network methodology is generally applicable to a variety of smart and interconnected health systems.
Random Forest Modeling for Survival Analysis of Breast Cancer Recurrences

Graduate student Farhad Imani and his CHOT project paper is accepted to the IEEE International Conference of Automation Science and Engineering 2019. 
The recurrence of breast cancer is a prevailing problem that decreases the quality of patients' lives, creates high burdens on the healthcare system, and impacts the well-being of society. Advanced sensing provides an unprecedented opportunity to increase information visibility and characterize patterns of event occurrences; yet, few previous works have investigated survival analysis of breast cancer recurrences based on large amount of data readily available in the health system. There is a dire need to leverage data to decipher important factors that play a role in the recurrence of breast cancer.
This paper presents an ensemble method of random survival forest for time-to-event analysis of breast cancer recurrences in the surveillance, epidemiology, and end results (SEER) data from year 1973 to 2015. The model characterizes the survival function among patients with and without recurrences of breast cancer. Ensemble models are constructed via sampling and bootstrapping into big data.
Experimental results show that the age when cancer recurrence happens and time-between-recurrences approximately follow the Gaussian and exponential distributions with the means of 61.35 and 2.61 years, respectively. In addition, the results show age, surgery status, stage of tumors, and histological grade are significant factors that influence the probability of breast cancer recurrences. The survival analysis approach shows strong potentials to help healthcare practitioners in prognosis, treatment, and decision-making of breast cancer recurrences.
This research is sponsored by Susan G. Komen Foundation via NSF CHOT. The team gratefully acknowledges the valuable contributions and suggestions from Dr. Jerome Jourquin and Dr. Stephanie Reffey for this research study.
Upcoming Events
Fall 2019 IAB Meeting
Hosted by the University of Washington 
Date: Thurs., Oct.17, 2019 and Fri., Oct.18, 2019
Location: University of Washington

More information can be found here.