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E – Health Prediction

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Predictive Analysis on Infectious Diseases Through Machine Learning

Infectious diseases today continue to pose a significant burden to health and society. Public Health Departments leads the surveillance and management of infectious diseases and environmental threats to health through the provision of specialist health protection, epidemiology and microbiology services and aims to detect possible outbreaks of disease and epidemics as rapidly as possible.

AI Offers Enormous Potential

The outbreak of infectious diseases among global population remains significantly enormous, and the pandemic preparedness capabilities necessary to confront such threats must be of greater potency. The techniques initiated should be able to apprehend which disease will newly emerge, and preparing counter measures for that disease. Artificial Intelligence (AI) offers new hope in not only effectively pre-empting, preventing and combating the threats of infectious disease epidemics, but also facilitating the understanding of health-seeking behaviours and public emotions during epidemics.

AI offers enormous potential for public health practitioners and policy makers to revolutionize healthcare and population health that promote cost-savings on therapeutic care, expand access to health information and services, and enhance individual responsibility for their health and well-being.

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Why Machine Learning is to be introduced in the health sector?

Identification of high-risk areas for deadly infectious and non-infectious disease outbreaks is very important so that prediction and detection of the deadly disease outbreaks can be conducted and responding to these deadly disease outbreaks can be made more effectively. This can be done by applying machine learning algorithms in predicting and detecting the deadly infectious disease and also in responding to the deadly infectious disease.

Making a very quick informed decision is very critical in order to reduce the damages caused by the impact of the disease outbreaks. The machine learning algorithms can be used to learn datasets that consist of information about known viruses, animal populations, human demographics, biology and biodiversity information, available physical infrastructures, cultural/social practices around the world and also the geolocation of the diseases to predict any outbreaks.

Machine learning methods can also learn integrated multi-sources data related to travel schedule, population, logistics and epidemiology data in order to predict the disease’s location and rate of spreading .

IVA is proposing the application of its Graph-ML platform, DataCivet, to this problem.  DataCivet can handle the entire prediction lifecycle from data ingestion to output dashboards automatically and without the intervention of expert ML personnel.  DataCivet is a developed product, and has been tested previously on health related problems.  Currently, in collaboration we are evaluating it for the epidemiology problem in collaboration with a Subject Matter Expert in Public Health.   Preliminary results indicate that this tool can provide a context agnostic approach to a variety of such epidemiological problems.

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