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Can Machine Learning Predict Depression?

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depby Angela Guess

Arshya Vahabzadeh recently wrote in The Huffington Post, “Can machine learning predict the extent and length of someone’s depression, right from the outset? In a study published in Molecular Psychiatry, baseline data from over a thousand people with major depressive disorder was analyzed. The aim was to predict the severity and chronicity of their depression. The authors compared the use of traditional analytics and machine learning approach. They not only found that machine learning could help predict the characteristics of a person’s depression, but also that it could do this more effectively, and with less information, than traditional approaches. The authors concluded that machine learning could be a clinically useful way to stratify depression.”

He continues, “How about using machine learning to link clinical depression with biomarkers? In one depression study, machine learning tools were used in addition to traditional statistics to analyze the relationship between 67 biomarkers in 5,227 research subjects. This hybrid technique was able to identify 3 biomarkers for depression, namely red cell distribution of width, serum glucose, and total bilirubin. Red cell distribution of width has been linked to inflammation in the body, and a line of research has linked together depression and inflammation. Serum glucose tests are used to screen for diabetes, a condition that has a complex relationship with depression, often occurring concurrently. Lastly it has been suggested that bilirubin is an antioxidant, and depression has been linked to oxidative stress.”

Read more here.

photo credit: Flickr/ AmateurArtGuy

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