Machine learning for treatment optimisation: part two

As artificial intelligence (AI) takes the world by storm, with the UK being ranked third globally for its research and innovation in this area (Crowcroft, 2022), machine learning is also growing as a broad subfield of AI. Machine learning is devoted to discovering and building methods that will allow machines to ‘learn’ and as we saw in last month's Treatments column, it is a developing area both within and beyond healthcare.

Last month's article explored a study examining machine learning to identify cohorts of a population that may benefit from specific medications. This month, we delve into a second study exploring the use of machine learning for the optimisation of patient treatment.

Taliaz et al (2021) explored the use of machine learning to optimise treatment for major depressive disorder (MDD), which is multifactorial, and poses a challenge to the tailoring of optimal medication for each patient. The sad reality is that most treatment relies on trial and error, with around half the patients receiving a good response and the other half either showing no greater effectiveness, or a worsening in their condition.