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Tag: Shahadat Uddin

PREDICTING CHRONIC DISEASES FROM HEALTHCARE DATA-A FRAMEWORK BASED ON GRAPH THEORY AND SOCIAL NETWORK MEASURES

by sofia | Posted on

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

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ADAPTING GRAPH THEORY AND SOCIAL NETWORK MEASURES ON HEALTHCARE DATA – A NEW FRAMEWORK TO UNDERSTAND CHRONIC DISEASE PROGRESSION

by sofia | Posted on

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

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APPLICATION OF NETWORK ANALYSIS ON HEALTHCARE

by sofia | Posted on

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

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SOCIAL NETWORK RESEARCH IN HEALTH COULD OPEN THE DOOR FOR SIGNIFICANT SAVINGS

by sofia | Posted on

The health industry consumes vast amounts of money and resources and is seen as a “black-hole” in many Government budgets. A ground breaking study using new science could save the industry millions and point the way for future research. Dr Shahadat Uddin CMCRC PhD Graduate Lecturer, U. Sydney Shahadat Uddin is a graduate of the

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SOCIAL NETWORK RESEARCH IN HEALTH COULD OPEN THE DOOR FOR SIGNIFICANT SAVINGS

by sofia | Posted on

The health industry consumes vast amounts of money and resources and is seen as a “black-hole” in many Government budgets. A ground breaking study using new science could save the industry millions and point the way for future research. Dr Shahadat Uddin CMCRC PhD Graduate Lecturer, U. Sydney Shahadat Uddin is a graduate of the

Read More

PREDICTING CHRONIC DISEASES FROM HEALTHCARE DATA-A FRAMEWORK BASED ON GRAPH THEORY AND SOCIAL NETWORK MEASURES

by sofia | Posted on

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

Read More

ADAPTING GRAPH THEORY AND SOCIAL NETWORK MEASURES ON HEALTHCARE DATA – A NEW FRAMEWORK TO UNDERSTAND CHRONIC DISEASE PROGRESSION

by sofia | Posted on

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

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A SOCIAL NETWORK FRAMEWORK TO EXPLORE HEALTHCARE COLLABORATION

by sofia | Posted on

A patient-centric approach to healthcare leads to an informal social network among medical professionals. This chapter presents a research framework to: identify the collaboration structure among physicians that is effective and efficient for patients; discover effective structural attributes of a collaboration network that evolves during the course of providing care; and explore the impact of

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APPLICATION OF NETWORK ANALYSIS ON HEALTHCARE

by sofia | Posted on

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

Read More