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Tag: health market quality

BIDIRECTIONAL LSTM-CRF FOR CLINICAL CONCEPT EXTRACTION

Extraction of concepts present in patient clinical records is an essential step in clinical research. The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for clinical records presented concept extraction (CE) task, with aim to identify concepts (such as treatments, tests, problems) and classify them into predefined categories. State-of-the-art CE approaches heavily rely on hand

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MULTI-FUND DATA ANALYTICS TARGETS ENHANCED VALUE-FOR-MONEY IN HEALTHCARE SERVICES

Governments as well as health and accident compensation insurers are grappling to improve health outcomes while keeping spiralling costs under control. These seemingly irreconcilable goals require a careful balance of policy to help stem the tide of ever increasing costs in the health industry. Dr Uma Srinivasan CMC Insurance Solutions CMC Lead Scientist Keywords: Health insurance,

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

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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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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CASE-MIX BASED PEER CLUSTERING

We consider the problem of clustering hospitals based on their case-mix distributions. Hospitals belong to the same cluster if they offer the same mix of services and have similar demand for those services. The cluster labels can be used to control for case-mix in hospital level analyses. Methods: We obtained distributions of the 770 AR-DRGv7.0 for

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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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