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Using Various Artificial Intelligence & Data Streams To Better Predict Flu

Influenza easily spreads and is highly contagious since individuals travel and move about, making forecasting and tracking flu activity a tricky part. While the CDC incessantly monitors people visits in the U.S. for flu-akin illness, this data can lag almost 2 Weeks behind actual time. A new survey, spearheaded by the CHIP (Computational Health Informatics Program) at Boston Children’s Hospital, mixes 2 forecasting techniques with artificial intelligence (machine learning) to predict domestic flu activity. Outcomes are posted in Nature Communications.

When the method, dubbed as ARGONet, was implemented to flu seasons between September 2014 and May 2017, it made more precise forecasts as compared to the team’s previous high-performing predicting method, ARGO, in over 75% of the states surveyed. This recommends that ARGONet makes the most precise estimates of influenza activity accessible till date, a week before conventional healthcare-supported reports, at the state level all over the U.S.

“Reliable and timely methodologies for tracing influenza activity all over locations can assist public health executives mitigate epidemic outbreaks and might enhance interaction with the public to increase awareness of possible dangers,” claimed Mauricio Santillana, the senior author of the paper.

On a related note, scientists have designed a novel AI system that can forecast how long an individual will be alive just by seeing at the pictures of their organs. The system, designed by experts in Australia from University of Adelaide, analyzed the medical imaging of chests of 48 patients and was successful to forecast which of them would expire in a timeframe of 5 years, with 69% correctness. This is analogous to ‘manual’ forecasts by clinicians, said researchers.

The report, published in the Scientific Reports journal, has suggestions for the premature analysis of medical intervention and serious illness. “Forecasting the potential of a patient is useful since it may allow doctors to modify treatments to the person,” claimed PhD student at the University of Adelaide, Luke Oakden-Rayner.

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