Sumary of New AI predictive algorithm greatly improves timeliness and accuracy of sepsis predictions:
- Each year, sepsis affects more than 30 million people worldwide, causing an estimated six million deaths.
- Sepsis is the body’s extreme response to an infection and is often life-threatening.
- Since every hour of delayed treatment can increase the odds of death by four to eight per cent, timely and accurate predictions of sepsis are crucial to reduce morbidity and mortality.
- To that end, various health care organizations have deployed predictive analytics to help identify patients with sepsis by using electronic medical record (EMR) data.
- Joseph’s Healthcare Hamilton, have created an Artificial Intelligence (AI) predictive algorithm that greatly improves the timeliness and accuracy of data-driven sepsis predictions.
- To predict sepsis in clinical care settings, some systems use EMR data with disease scoring tools to determine sepsis risk scores – essentially acting as digital, automated assessment tools.
- More advanced systems employ predictive analytics, such as AI algorithms, to go beyond risk assessment and identify sepsis itself.
- Using AI predictive analytics, researchers created an algorithm called the Bidirectional Long Short-Term Memory (BiLSTM).