To manage the condition, severe asthma needs rigorous pharmaceutical therapy. Although oral corticosteroids are efficient, there are significant adverse effects associated with their administration. Although it has been demonstrated that biologics that target the distinct inflammatory mechanisms that underlie the illness are beneficial, not all individuals respond to them similarly. Because biologics are costly medications, the failure to anticipate responders significantly impacts healthcare expenses as we treat more patients than those who can react. Therefore, it would be preferable to choose the “right patients” more precisely to provide the “appropriate biologics.”
It was feasible to improve the capacity to anticipate outcomes in patients treated with biologics, as demonstrated by machine learning approaches. Using cluster analysis, researchers recently discovered 4 distinct clusters with varied benralizumab responses within the T2 high phenotype. The greatest response rate (80–90%) was seen in two of these clusters, which were characterized by greater levels of inflammatory markers.
For asthma research, machine learning holds promise since it would allow them to forecast which patients will respond to which medication. The methods could speed up the diagnosis process and raise the likelihood that the best possible course of action will be chosen for each individual patient, improving patient care and satisfaction.