Patient Subgroup

Identifying patient subgroups with distinct treatment responses is crucial for improving healthcare outcomes. Research focuses on developing methods, often employing machine learning (including neural networks and inverse propensity weighting) and unsupervised learning techniques, to identify these subgroups within both randomized controlled trials and observational studies, addressing challenges posed by data heterogeneity and treatment non-randomization. This work aims to enable more precise treatment effect estimations and personalized medicine strategies, ultimately leading to improved treatment efficacy and resource allocation. The ultimate goal is to translate these findings into clinically actionable insights for better patient care.