PhD Health Market Quality candidate Arif Khan has been published in the International Journal of Medical Informatics alongside one of the co-authors, CMCRC HMQ senior research scientist and PhD mentor Dr Uma Srinivasan. Their research has uncovered how using and analysing 1.4 million electronic medical records from almost 1 million de-identified patients can help determine when
The paper presents an approach that applies social network theory to understand chronic disease progression. Submitted to the Australasian Workshop on Health Informatics and Knowledge Management https://cs.anu.edu.au/conf/acsw2016/sub-confs/hikm.html Author(s): Arif Khan, Shahadat Uddin and Uma Srinivasan
Fei Wang, Uma Srinivasan, Shahadat Uddin, and Sanjay Chawla. “Application of network analysis on healthcare”. In Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on IEEE, 2014. The healthcare sector holds large amounts of semantically rich electronic data generated and used by different sections of the health care community. Data analytic
The study illustrates a framework to predict the progression of chronic diseases from a new perspective using graph theory and social network analysis methods. The framework utilizes large and untapped longitudinal administrative data sets that contain ICD-10-AM disease codes that describe the principal and secondary diagnosis recorded during hospital admissions. The primary focus of the
The paper presents an approach that applies social network theory to understand chronic disease progression. Submitted to the Australasian Workshop on Health Informatics and Knowledge Management https://cs.anu.edu.au/conf/acsw2016/sub-confs/hikm.html Author(s): Arif Khan, Shahadat Uddin and Uma Srinivasan