研究人员开发一个机器学习模型,以83%的精确度预测慢性疲劳综合症,加速诊断。 Researchers develop a machine learning model that predicts chronic fatigue syndrome with 83% accuracy, speeding up diagnosis.

墨尔本大学的研究人员创造了一个机器学习模型,通过分析28个生物标志和症状,能够以83%的精确度预测慢性疲劳综合症(CFS)。 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. 这可以大大加快诊断速度,因为由于缺乏确切的测试,往往需要数年时间才能确诊。 This could significantly speed up diagnosis for a condition that often takes years to identify due to lack of a definitive test. 该模型最终可用于政府采购办公室,以帮助更快、更准确的诊断。 The model could eventually be used in GP offices to aid quicker and more accurate diagnoses.