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Author: sofia

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

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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A FRAMEWORK FOR ADMINISTRATIVE CLAIM DATA TO EXPLORE HEALTHCARE COORDINATION AND COLLABORATION

Previous studies have documented the application of electronic health insurance claim data for health services research purposes. In addition to administrative and billing details of healthcare services, insurance data reveal important information regarding professional interactions/links that emerge among healthcare service providers through, for example, informal knowledge sharing. By utilising details of such professional interactions and

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JOINT PREDICTION OF CHRONIC CONDITIONS ONSET: COMPARING MULTIVARIATE PROBITS WITH MULTICLASS SUPPORT VECTOR MACHINES

We consider the problem of building accurate models that can predict, in the short term (2-3 years), the onset of one or more specific chronic conditions at individual level. Methods: We consider 5 chronic conditions: heart disease, stroke, diabetes, hypertension and cancer and build two different models that predict all possible combinations of these conditions. Covariates

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AN APPROACH TO EMPOWER PATIENTS TO MANAGE DIABETES

Timely information and education can enhance the ability of consumers to make informed choices about their health, lifestyle and modifiable disease risk factors. Due to its unstructured and varied format, and lack of targeted delivery methods, information and knowledge does not often reach consumers, when they need it most. The aim of this project is

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

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

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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LEVERAGING BIG DATA ANALYTICS TO REDUCE HEALTHCARE COSTS

The healthcare sector deals with large volumes of electronic data related to patient services. This article describes two novel applications that leverage big data to detect fraud, abuse, waste, and errors in health insurance claims, thus reducing recurrent losses and facilitating enhanced patient care. The results indicate that claim anomalies detected using these applications help

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ANOMALIES DETECTION IN HEALTHCARE SERVICES

Srinivasan, U. “Anomalies Detection in Healthcare Services” Using several practical examples of cost and quality-of-care outliers, the author presents a framework to detect outliers and anomalies in healthcare services. Author(s): Srinivasan, U. View Paper

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LEVERAGING BIG DATA ANALYTICS TO REDUCE HEALTHCARE COSTS

The healthcare sector deals with large volumes of electronic data related to patient services. This article describes two novel applications that leverage big data to detect fraud, abuse, waste, and errors in health insurance claims, thus reducing recurrent losses and facilitating enhanced patient care. The results indicate that claim anomalies detected using these applications help

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