Disease prediction system using open source data

Abstract

Described is a disease prediction system using open source data. The system includes a preprocessing module, a learning module, and a prediction module. The preprocessing module receives a dataset of N trend results related to a disease event and generates an enhanced filter signal (EFS) curve related to the disease event. The learning module receives the EFS curve and generates a predicted number of cases of the disease e vent and, using a plurality of machine learning methods, generates a plurality of predictions that the disease event will happen within a future time period. The prediction module determines precision and recall for each of the plurality of predictions and, based on the precision and recall, provides a likelihood that the disease event will occur.

Description

[0001] government rights [0002] This invention was made with Government support under US Government Contract IARPA OSI-D12PC00285. The government has certain rights in this invention. [0003] Cross References to Related Applications [0004] This application is a non-provisional patent application claiming priority to US Provisional Application No. 61 / 941,920, filed February 19, 2014, entitled "Predict Rare Disease Using Open Source Data." technical field [0005] The present invention relates to prediction systems, and more particularly, to systems that use open source data to predict disease. Background technique [0006] Prevention of infectious diseases and timely detection of health threats are global health priorities. Securing the line of defense against infectious disease is important when rapid response following early detection of disease activity can reduce the social and medical impact of disease. However, traditional surveillance systems such as the Cente...

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Problems solved by technology

However, traditional surveillance systems such as the Centers for Disease Control and Prevention (CDC) rely on clinical data

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