"In a study published in Molecular Psychiatry, baseline data from over a thousand people with major depressive...
Originally shared by Wayne Radinsky
"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."
"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."
"By using machine learning to evaluate clinical data, the researchers were able to produce three different machine learning algorithms that could distinguish between people that had attempted suicide, and those that had not, based on the patient's prior clinical data. The prediction accuracy varied between 65-72%."
"One study looking at 4041 patients with depression, found that by using a machine learning approach, they could predict response to the antidepressant citalopram with a highly statistically significant accuracy of 64.6%."
"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."
"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."
"By using machine learning to evaluate clinical data, the researchers were able to produce three different machine learning algorithms that could distinguish between people that had attempted suicide, and those that had not, based on the patient's prior clinical data. The prediction accuracy varied between 65-72%."
"One study looking at 4041 patients with depression, found that by using a machine learning approach, they could predict response to the antidepressant citalopram with a highly statistically significant accuracy of 64.6%."
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