Differences in drug responses

Previous reviews mostly assessed the prediction accuracy of machine learning models. However, even using models with nearly 100% accuracy, it is difficult to be accepted by clinicians unless there is a treatment-by-group interaction between a specific drug and subgroups generated by this model. Algorithms divided cohorts into subgroups and “p” for interaction between drug effect and subgroup was assessed. This process serves to underline the clinical utility of the algorithms.^47,50 For decision-making, randomized controlled trials (RCTs) offer high-level clinical evidence. Therefore, subgroup analysis or post hoc analysis can provide exploratory evidence for the algorithm's applicability in drug selection, but is not sufficient enough to bring changes to current clinical practice. In cohorts that have not been randomized, alternative comparisons may also uncover potential treatment differences among drugs. In a study using EHR, the patients on SGLT2i and dipeptidyl peptidase 4 inhibitors (DPP4i) were matched using propensity scoring, and the class effect of these drugs on renal function preservation was examined. ⁶⁸ These methods could potentially be used to assess the treatment-by-group interaction in machine learning-identified subgroups.