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Tag: #Diabetesresearch #diabetes #diabetesT2

HEALTH PHD STUDENT ARIF KHAN HAS BEEN PUBLISHED IN THE INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

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

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JOINT PREDICTION OF ONSET CHRONIC CONDITIONS

Chronic conditions can be costly but also preventable as well as predictable. We develop a model to predict in the short term (2-3 years) the onset of one or more chronic conditions. Five chronic conditions are considered: heart disease, stroke, diabetes, hypertension and cancer. Predictions are made on the basis of standard demographic/socio-economic variables, risk

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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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UNDERSTANDING CHRONIC DISEASE COMORBIDITIES FROM BASELINE NETWORKS – KNOWLEDGE DISCOVERY UTILISING ADMINISTRATIVE HEALTHCARE DATA

Hospitals routinely collect admitted patients’ data for administrative purposes and for reporting to the government and health insurers. These heterogeneous and mostly untapped data contain rich semantic information about patients’ health conditions in the form of standard disease codes. These traces of clinical information can be aggregated over patients to understand how their health progresses

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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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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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DYNAMIC SIMULATION MODELLING FOR GUIDING ACTION ON CHILDHOOD OVERWEIGHT AND OBESITY

Speaker: Vincy Li, NSW Ministry of Health Seminar Date: Tuesday July 18 12:00pm Brief abstract: The increasing prevalence of childhood overweight and obesity raises significant concerns about the effect on individuals’ health, society and the economy. Childhood overweight and obesity is a complex problem, with many inter-related causes and points for intervention. There is, however, little consensus over which

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