TY - JOUR
T1 - Directed disease networks to facilitate multiple-disease risk assessment modeling
AU - Wang, Tingyan
AU - Qiu, Robin G.
AU - Yu, Ming
AU - Zhang, Runtong
N1 - Funding Information:
This project was partially supported by the key project of National Natural Science Foundation of China (Big Data Driven Innovation and Management of Intelligent Healthcare, Grant No.: 71532002 ), IBM Faculty Awards ( RDP-Qiu2016 and RDP-Qiu2017 ), and Penn State ICS Seed Grants ( Deep Learning, 2018–19 and Reinforcement Learning, 2019–2020 ). Tingyan Wang received a Ph.D. in Management Science and Engineering from Tsinghua University in June 2018. She is now a postdoctoral researcher in the Nuffield Department of Medicine at the University of Oxford. She is currently also appointed in an honorary capacity as Data Analyst by Oxford University Hospitals NHS Trust. Most of her work has been focused on patients' electronic health records analysis and risk predictive modeling by leveraging statistics, machine learning and deep learning techniques. Her research interests include Healthcare Analytics, Medical Informatics, Disease Risk Prediction, Longitudinal Data Analysis, and Patient Care Process Analysis. Robin Qiu holds a Ph.D. in Industrial Engineering and a Ph.D. (minor) in Computer Science both from The Pennsylvania State University. He is currently Director of Big Data Lab and Professor of Information Science at Penn State. He has had over 180 peer-reviewed publications, including 3 books. He is on the advisory board of Service Science and serves as an associate editor of IEEE Transactions on Systems, Man and Cybernetics and IEEE Transactions on Industrial Informatics . He was the Editor-in-Chief of Service Science and the Editor-in-Chief of International Journal of Services Operations and Informatics . He was the founding chair of the Logistics and Services Technical Committee, IEEE Intelligent Transportation Systems Society and the founding chair of Service Science Section of the INFORMS. His research interests include Big Data, Data Analytics, Smart Service Systems, Service Science, Service Operations and Management, Information Systems, and Manufacturing and Supply Chain Management. Ming Yu holds a Ph.D. Ph.D. in Industrial Engineering from the National University of Ireland. He is currently Associate Professor in the Department of Industrial Engineering at Tsinghua University. He has been studying how to use industrial engineering especially information technology to the healthcare field from 2004 and he has intensive cooperation with multiple hospitals in China. He has publications in Journal of biomedical informatics, BMC Medical Informatics, and Decision Making. He has been working on the coding system development of the ICD-10 simplified version applied in Asia Pacific area for WHO Family of International Classifications (WHO-FIC) from 2015. He was an active member of the Program Board in several international conferences. He has been a Professional Member in Chinese Mechanical Engineering Association (CMES), and Institute Engineering, Ireland (MIEI). His research interests include Medical Informatics, Natural Language Processing, System Engineering, and Management Information System. Runtong Zhang was born in November 1963, in Chaoyang, Liaoning, China. He got his Ph.D. in Production Engineering and Management from Technical University of Crete in Greece in 1996, and his B.S. in Computer Science and Automation from the Dalian Maritime University in China in 1985, respectively. He is presently a professor and head of the Department of Information Management at Beijing Jiaotong University, China. He was also with the Swedish Institute of Computer Science as a senior researcher, and the Port of Tianjin Authority as an engineer. His current research interests include big data, health-care management, operations research and artificial intelligence. He has published over 300 papers in referenced journals and conferences, and 40 books. He has been a PI for over 100 research projects and is a holder of 9 patents. He has been Senior Member, IEEE and a general chair or co-chair for over 10 IEEE sponsored international conferences.
Funding Information:
This project was partially supported by the key project of National Natural Science Foundation of China (Big Data Driven Innovation and Management of Intelligent Healthcare, Grant No.: 71532002), IBM Faculty Awards (RDP-Qiu2016 and RDP-Qiu2017), and Penn State ICS Seed Grants (Deep Learning, 2018?19 and Reinforcement Learning, 2019?2020).
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/2
Y1 - 2020/2
N2 - We investigate multiple disease risk prediction modeling, aimed at assessing future disease risks for an individual who is ready for discharge after hospitalization. We propose a novel framework that combines directed disease network and recommendation system techniques to substantially enhance multiple disease risk predictive modeling. Firstly, a directed disease network considering temporal information is developed. Then based on this directed disease network, we look into different disease risk score computing approaches. We validate the proposed approaches with two real-world datasets from two independent hospitals. The predicted results can be promisingly utilized as a reference for medical experts to offer effective healthcare guidance for both inpatients and outpatients. The proposed framework can also be utilized for developing an innovative tool that helps individuals create and maintain a better healthcare plan over time.
AB - We investigate multiple disease risk prediction modeling, aimed at assessing future disease risks for an individual who is ready for discharge after hospitalization. We propose a novel framework that combines directed disease network and recommendation system techniques to substantially enhance multiple disease risk predictive modeling. Firstly, a directed disease network considering temporal information is developed. Then based on this directed disease network, we look into different disease risk score computing approaches. We validate the proposed approaches with two real-world datasets from two independent hospitals. The predicted results can be promisingly utilized as a reference for medical experts to offer effective healthcare guidance for both inpatients and outpatients. The proposed framework can also be utilized for developing an innovative tool that helps individuals create and maintain a better healthcare plan over time.
UR - http://www.scopus.com/inward/record.url?scp=85074422233&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074422233&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2019.113171
DO - 10.1016/j.dss.2019.113171
M3 - Article
AN - SCOPUS:85074422233
SN - 0167-9236
VL - 129
JO - Decision Support Systems
JF - Decision Support Systems
M1 - 113171
ER -