AI model predicts patient decline with near-perfect accuracy using facial expressions

News-Medical.Net August 13, 2024
Priyanjana Pramanik, MSc.

In a recent study published in the journal Informatics, researchers investigated the use of advanced machine learning methods to recognize facial expressions as indicators of health deterioration in patients.

Their findings indicate that the developed Convolutional Long Short-Term Memory (ConvLSTM) model can accurately predict health risks with an impressive 99.89% accuracy, which could enhance early detection and improve patient outcomes in hospital settings.

Background

Facial expressions are crucial to human communication, conveying emotions and non-verbal cues across different cultures. Charles Darwin first explored the idea that facial movements reveal emotions, and later research by Ekman and others identified universal facial expressions linked to specific emotions.

The Facial Action Coding System (FACS), developed by Ekman and Friesen, became a vital tool...

Today's Sponsors

Today's Sponsor