and anxiety symptoms are common among university students in many regions of
the world. According to the latest Center for Collegiate Mental Health report,
anxiety and depression are the top reasons that college students seek
counseling. The trend has been growing over the last four years.
Mental health problems like anxiety and depression can
interfere with a student’s studies and hinder performance. Depression is
associated with poor academic performance and dropping out of school. Traditionally
clinicians have interviewed patients, asking questions about mood, lifestyle,
and previous mental problems to identify whether a patient is depressed or not.
That method might be something of the past. Machine learning might step in to
diagnose depression in patients.
In recent years, machine learning has emerged as a possible tool
to diagnose depression. Machine
learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from experience
without being explicitly programmed.
MIT News reports
that machine-learning models have been developed that can detect words and
intonations in speech that may indicate depression. Although these methods are
accurate, there’s a limit to them as they depend on specific answers to
specific questions to make the diagnosis.
MIT researchers have developed a neural-network model that
can scrutinize raw text and audio data from interviews to discover speech
patterns that may point to depression. This model doesn’t need information
about questions and answers to make an accurate prediction.
further development, this model could be deployed in smartphone apps where the
model would monitor a user’s text and voice for signs of emotional and mental
distress and then alert someone appropriate. This could be a boon to people who
are suffering from depression but don’t realize that what they are going
through is depression and that they need treatment for it.
Machine learning predicts severity and length
analyzed baseline data from over a thousand people with Major Depressive
Disorder (MDD) to predict the severity and length of the participants’
depression. The researchers compared the use of traditional analytics and a machine
learning approach and found that the machine learning approach was superior. Machine
learning could predict the characteristics of a person’s depression more
effectively using less information than traditional analytics.
Machine learning links clinical depression with
learning has also been employed to link clinical depression with biomarkers. In
published in PLoS One, researchers used machine learning tools and traditional
statistics to analyze the relationship between 67 biomarkers in 5,227 research
subjects. Three biomarkers for depression were found: red cell distribution of
width, serum glucose, and total bilirubin.
Machine learning and the detection of suicide
There is a
close link between depression and suicide and it is notoriously difficult to
predict suicide. Is it possible that machine learning can help out here as
well? One pilot study used
machine learning to look at the clinical data from 144 patients with mood
researchers use clinical variables associated with suicide attempts among
patients with mood disorders and other variables to ‘train’ a machine learning
algorithm. The resulting algorithm was then used on ‘new’ subjects to identify
them as either suicidal or not. The researchers came up with three machine
learning algorithms that could distinguish between people who had attempted
suicide and those that had not. The accuracy rate varied between 65% and 72%.
into the application of machine learning to detect the presence of depression
in people is ongoing and shows much promise.
Credit: Google News