Healthcare information technology system for predicting development of cardiovascular conditions

Abstract

Described herein is a framework for predicting development of a cardiovascular condition of interest in a patient. The framework involves determining, based on prior domain knowledge relating to the cardiovascular condition of interest, a risk score as a function of patient data. The patient data may include both genetic data and non-genetic data. In one implementation, the risk score is used to categorize the patient into at least one of multiple risk categories, the multiple risk categories being associated with different strategies to prevent the onset of the cardiovascular condition. The results generated by the framework may be presented to a physician to facilitate interpretation, risk assessment and / or clinical decision support.

Description

[0001] This application is a continuation-in-part of USSN 12 / 506,583 filed July 21,2009. This application also claims the benefit of U.S. Provisional Application 61 / 313,446, filed March 12, 2010, and U.S. Provisional Application 61 / 392,156, filed October 12, 2010; References are incorporated herein. technical field [0002] The present disclosure relates broadly to healthcare information technology (HIT) systems, and more specifically, to an integrated diagnostic model for assessing a patient for cardiovascular disease. Background technique [0003] Hypertension, or high blood pressure, has been recognized as an important risk factor for vascular morbidity and mortality. Among the life-threatening cardiovascular diseases that can result from persistent hypertension, stroke and myocardial infarction (i.e. heart attack) are associated with the highest relative risk from hypertension and thus contribute largely to blood pressure-related mortality . Research has shown that 13...

AI Technical Summary

Problems solved by technology

However, little work has been done on building statistical and data mining-based models for predicting hypertension

Furthermore, the challenge facing such endeavors is the lack of high-quality data that can be extracted and analyzed in any meaningful or reliable manner

This is because most of the predictor variables are usually incomplete due to imperfect data collection process, lack of precise assessment and lack of awareness of patient factors, cost constraints associated with equipment, etc.

Most forecasting methods fail when there are missing data or values

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