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Tag: chronic disease

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

by sofia | Posted on

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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JOINT PREDICTION OF ONSET CHRONIC CONDITIONS

by sofia | Posted on

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

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

by sofia | Posted on

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

by sofia | Posted on

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

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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