What Can Machine Learning Teach Us About Mental Health?

Artificial Intelligence is radically transforming virtually every aspect of our society, and our mental health system is no exception. While many individuals might have reservations about handing over the care of vulnerable patients to “robots,” machine learning can teach us a surprising amount about mental health in Australia. While Artificial Intelligence can probably never replace a qualified psychiatrist who is able to treat patients directly, it can be a useful tool. Perhaps the most promising benefit of AI is the fact that it can accurately analyse data and help us come to useful conclusions. So what can machine learning teach us about mental health?

The Factors That Can Lead To Becoming a High Mental Health Utiliser

After machine learning was applied to EHR data, researchers were able to identify several factors that appear to increase the likelihood of someone becoming a high mental health utiliser. A high mental health utiliser is someone who is admitted to an inpatient psychiatric hospital more than three times per year.

Some of the conclusions made by researchers were fairly obvious; high utilisers are more likely to have mental illnesses such as schizophrenia. On the other hand, other factors might not be so obvious. According to the data, high utilisers also tend to come from socially disadvantaged groups, and they have limited access to community-based services.

The Top Predictors of High Utilisation

According to the data, the top predictors of high utilisation were the amount of education an individual had received, a schizophrenia diagnosis, and personality disorder diagnoses. While the latter two predictors are again fairly obvious, many might not have expected such a strong link between poor education and mental health.

How Machine Learning Works in the Context of Mental Health

When analyzing EHR data in this situation, the machine learning system used a specific algorithm known as an “elastic net.” Compared to a normal analysis method, this allowed researchers to predict utilization by including all of the predictors in the same model, at the same time. This helps researchers avoid unstable estimates, especially in regard to the importance of each predictor’s connection with high utilization. With the elastic net, researchers can quantify each predictor’s relationship to the outcome, improving the accuracy of their conclusions significantly.

Why It’s Important

Machine learning in mental health can be an incredibly useful tool in the future. Understanding the factors that lead to high utilization can allow us to take effective action and reduce the strain on the healthcare system. In many first-world countries such as Australia, as little as 5% of the population can consume up to 50% of the nation’s healthcare resources. After identifying the predictors for high utilization, we can take steps to stop the trend and make our healthcare system more efficient.

While we should never look at a psychiatric patient as “just another number,” the data is important. If we can use machine learning to provide more accurate and effective assistance to those in need, then it could be one of the most powerful tools at our disposal. Not only can machine learning lessen the financial burden on the healthcare system in Australia, but it can also make our methods more targeted and efficient.

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