The burden of diseases is rising worldwide, with unequal treatment efficacy
for patient populations that are underrepresented in clinical trials.
Healthcare, however, is driven by the average population effect of medical
treatments and, therefore, operates in a "one-size-fits-all" approach, not
necessarily what best fits each patient. These facts suggest a pressing need
for methodologies to study individualized treatment effects (ITE) to drive
personalized treatment. Despite the increased interest in
machine-learning-driven ITE estimation models, the vast majority focus on
tabular data with limited review and understanding of methodologies proposed
for time-series electronic health records (EHRs). To this end, this work
provides an overview of ITE works for time-series data and insights into future
research. The work summarizes the latest work in the literature and reviews it
in light of theoretical assumptions, types of treatment settings, and
computational frameworks. Furthermore, this work discusses challenges and
future research directions for ITEs in a time-series setting. We hope this work
opens new directions and serves as a resource for understanding one of the
exciting yet under-studied research areas.

研究人员综述了关于时间序列数据的个体化治疗效应研究，并对理论假设、治疗设置类型和计算框架进行了评论，讨论了时间序列环境下个体化治疗效应研究面临的挑战和未来研究方向。