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Researchers develop a machine learning model that predicts chronic fatigue syndrome with 83% accuracy, speeding up diagnosis.
Researchers at the University of Melbourne have created a machine learning model that can predict chronic fatigue syndrome (CFS) with 83% accuracy by analyzing 28 biomarkers and symptoms.
This could significantly speed up diagnosis for a condition that often takes years to identify due to lack of a definitive test.
The model could eventually be used in GP offices to aid quicker and more accurate diagnoses.
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Los investigadores desarrollan un modelo de aprendizaje automático que predice el síndrome de fatiga crónica con un 83% de precisión, acelerando el diagnóstico.