Machine learning for precision treatment: part one

Multiple medications exist for the same condition and not everyone is suitable to take the same drug for the same health problem. Machine learning is advancing in its efficacy for assessing and helping treat patients in various ways.

This instalment, which is focused on machine learning, will delve into a new study published in the journal Heart, which examines the use of machine learning to identify cohorts of a population that may benefit from specific medications. Next month’s column will explore a second study on the use of machine learning for optimising treatment.

In a study published in Heart, Sotomi et al (2023) examined the use of machine learning to identify cohorts of a population that may benefit from specific medications. The team had previously established that a machine learningbased clustering model was able to classify heart failure with preserved ejection fraction (HFpEF) into four distinct phenotypes. Depending on the phenotype, the person may suit a particular medication, and thus, specific medications may be found to have good efficacy in each of the four phenotypes. Therefore, the researchers assessed whether this was the case, as this could have positive implications for targeting certain populations. By administering correct treatment at an earlier stage in their heart failure, it may be possible to improve longer-term outcomes.