Transfer Learning for Individualized Treatment Rules: Application to Sepsis Patients Data from eICU-CRD and MIMIC-III Databases

TL;DR

This paper develops a transfer learning approach to create individualized treatment rules for sepsis patients, addressing population heterogeneity by combining data from multiple sources to improve personalized medical decision-making.

Contribution

It introduces a novel transfer learning algorithm using CAIPW and GA to derive targeted, interpretable ITRs from heterogeneous datasets in healthcare.

Findings

Successfully applied to eICU-CRD and MIMIC-III databases

Identified key covariates and optimal treatment strategies for sepsis

Enhanced generalizability of treatment rules across populations

Abstract

Modern precision medicine aims to utilize real-world data to provide the best treatment for an individual patient. An individualized treatment rule (ITR) maps each patient's characteristics to a recommended treatment scheme that maximizes the expected outcome of the patient. A challenge precision medicine faces is population heterogeneity, as studies on treatment effects are often conducted on source populations that differ from the populations of interest in terms of the distribution of patient characteristics. Our research goal is to explore a transfer learning algorithm that aims to address the population heterogeneity problem and obtain targeted, optimal, and interpretable ITRs. The algorithm incorporates a calibrated augmented inverse probability weighting (CAIPW) estimator for the average treatment effect (ATE) and employs value function maximization for the target population using Genetic Algorithm (GA) to produce our desired ITR. To demonstrate its practical utility, we apply this transfer learning algorithm to two large medical databases, Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). We first identify the important covariates, treatment options, and outcomes of interest based on the two databases, and then estimate the optimal linear ITRs for patients with sepsis. Our research introduces and applies new techniques for data fusion to obtain data-driven ITRs that cater to patients' individual medical needs in a population of interest. By emphasizing generalizability and personalized decision-making, this methodology extends its potential application beyond medicine to fields such as marketing, technology, social sciences, and education.