A perspective on individualized treatment effects estimation from time-series health data.

Abstract

Objectives: The objective of this study is to provide an overview of the current landscape of individualized treatment effects (ITE) estimation, specifically focusing on methodologies proposed for time-series electronic health records (EHRs). We aim to identify gaps in the literature, discuss challenges, and propose future research directions to advance the field of personalized medicine.

Materials and methods: We conducted a comprehensive literature review to identify and analyze relevant works on ITE estimation for time-series data. The review focused on theoretical assumptions, types of treatment settings, and computational frameworks employed in the existing literature.

Results: The literature reveals a growing body of work on ITE estimation for tabular data, while methodologies specific to time-series EHRs are limited. We summarize and discuss the latest advancements, including the types of models proposed, the theoretical foundations, and the computational approaches used.

Discussion: The limitations and challenges of current ITE estimation methods for time-series data are discussed, including the lack of standardized evaluation metrics and the need for more diverse and representative datasets. We also highlight considerations and potential biases that may arise in personalized treatment effect estimation.

Conclusion: This work provides a comprehensive overview of ITE estimation for time-series EHR data, offering insights into the current state of the field and identifying future research directions. By addressing the limitations and challenges, we hope to encourage further exploration and innovation in this exciting and under-studied area of personalized medicine.