Could artificial intelligence, machine learning help detect sepsis early?

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Driven by the nation’s aging population, the rise of drug-resistant bacteria, and the growth of medical interventions, sepsis has become one of the most common reasons for hospitalization in the U.S.

[M]any health research organizations have begun to implement various screening tools that can send an alert or alarm to a clinician or nurse when a patient meets certain criteria that classify them as high risk.

[A] computer algorithm they developed could sift through patients’ records and predict septic shock—the deadliest version of sepsis—in 85% of cases, usually more than a day before onset. Two-thirds of the time the system predicted sepsis before it inflicted any damage. That’s 60% better detection performance compared to current screening tests without raising the rate of false alerts.

Targeted Real-time Early Warning System, or TREWS, aims to deploy these machine-learning methods inside hospitals to help save lives.

The researchers realized early on that AI algorithms that leverage so-called Bayesian techniques in probability and statistics might do the trick because “they are fundamentally suited to dealing with uncertainty,” [researcher Suchi] Saria says. Bayesian machine learning allows researchers to encode in models their prior beliefs about what those models should look like and how they should behave. Then, as additional information comes in, they can update those beliefs.

The GLP aggregated and excerpted this blog/article to reflect the diversity of news, opinion, and analysis. Read full, original post: Using Artificial Intelligence to Spot Hospitals’ Silent Killer

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