An RKHS approach to estimate individualized treatment rules based on functional predictors

Jun Fan, Fusheng Lv, Lei Shi*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

In recent years there has been massive interest in precision medicine, which aims to tailor treatment plans to the individual characteristics of each patient. This paper studies the estimation of individualized treatment rules (ITR) based on functional predictors such as images or spectra. We consider a reproducing kernel Hilbert space (RKHS) approach to learn the optimal ITR which maximizes the expected clinical outcome. The algorithm can be conveniently implemented although it involves infinite-dimensional functional data. We provide convergence rate for prediction under mild conditions, which is jointly determined by both the covariance kernel and the reproducing kernel.
Original languageEnglish
Pages (from-to)169-181
Number of pages13
JournalMathematical Foundations of Computing
Volume2
Issue number2
DOIs
Publication statusPublished - Jul 2019

User-Defined Keywords

  • Learning theory
  • individualized treatment rules
  • functional data
  • reproducing kernel Hilbert space

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