Artificial intelligence is being used to detect and predict depression in people in a University of Newcastle research project that aims to improve quality of life.
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Associate Professor Raymond Chiong's research team has developed machine-learning models that "detect signs of depression using social media posts with over 98 per cent accuracy".
"We have used machine learning to analyse social media posts such as tweets, journal entries, as well as environmental factors such as demographic, social and economic information about a person," Dr Chiong said.
This was done to detect if people were suffering from depression and to "predict their likelihood of suffering from depression in the future".
Dr Chiong said early detection of depression and poor mental health can "prevent self-harm, relapse or suicide, as well as improve the quality of life" of those affected.
"More than four million Australians suffer from depression every year and over 3000 die from suicide, with depression being a major risk factor," he said.
People often use social media to "express their feelings" and this can "identify multiple aspects of psychological concerns and human behaviour".
The next stage of the team's research will involve "detecting signs of depression by analysing physiological data collected from different kinds of devices".
"This should allow us to make more reliable and actionable predictions/detections of a person's mental health, even when all data sources are not available," he said.
"Data from wearable devices such as activity measurements, heart rate and sleeping patterns can be used for behaviour and physiological monitoring.
"By combining and analysing data from these sources, we can potentially get a very good picture of a person's mental health."
The goal is to make such tools available on a smartphone application, which will allow people to regularly monitor their mental health and seek help in the early stages of depression.
"Such an app will also build the ability of mental health and wellbeing providers to integrate digital technologies when monitoring their patients, by giving them a source of regular updates about the mental health status of their patients," he said.
"We want to use artificial intelligence and machine learning to develop tools that can detect signs of depression by utilising data from things we use on a regular basis, such as social media posts, or data from smartwatches or fitness devices."
The research team aims to develop smartphone apps that can be used by mental health professionals to better monitor their patients and help them provide more effective treatment.
Depression Doesn't Discriminate
The overarching goal of the research is to "improve quality of life".
"Depression can seriously impact one's enjoyment of life. It does not discriminate - anyone can suffer from it," Dr Chiong said.
"To live a high quality of life, one needs to be in good mental health. Good mental health helps people deal with environmental stressors, such as loss of a job or partner, illness and many other challenges in life."
The technology involved can help people monitor how well they are coping in challenging circumstances.
This can encourage them to seek help from family, friends and professionals in the early stages of ailing mental health.
By doing so, professionals could help people prone to depression and other mental illnesses well before the situation becomes risky.
"They could also use this technology to get more information about their patients, in addition to what they can glean during consultation," he said.
This makes early interventions possible and "reduces the likelihood of self-harm or suicide attempts".
Depending on funding, the team plans to work on integrating people's health data from smart-fitness devices, such as heart rate, sleeping patterns and physical activity.
The intention is to work with Hunter New England mental health professionals on this stage of the research.
"Following this, our goal is to develop a smartphone app that can not only be used by clinical practitioners, but also everyday individuals to monitor their mental health status in real time."
He said machine learning models had shown "great potential in terms of learning from training data and making highly accurate predictions".
"For example, the application of machine learning/deep learning for image recognition is a major success story," he said.
Studies have shown that machine learning had "enormous potential in the field of mental health as well".
"The fact that we were able to obtain more than 98 per cent accuracy in detecting signs of ill mental health demonstrates that there is great potential for machine learning in this field."
However, he said the technology does face challenges before it can be applied in real-world scenarios.
"Some mobile apps have been developed that use machine learning to provide customised physical or other activities for their users, with the goal of helping them stay in good mental health," he said.
"However, our proposed app will be one of the first that allows users to monitor their mental health status in real time, by analysing their social media posts and health measurements."
Clinical practitioners could use this app to monitor their patients, but convincing them to use the technology will be one of the challenges.
