Thanks to modern medical devices, clinicians are able to obtain accurate and frequent measurements of the patient’s physiological state. Precision medicine aims to individualize the treatment for each patient and design optimal treatment regimes, using the vast amount of data stored in EHRs. Learning individualized treatment responses accurately is an essential step to achieve the goals of precision medicine. In the literature, the majority of treatment response methods use parametric functions to model the response curves. The functions are designed using domain knowledge about the clinical behavior of the treatment and make strong assumptions about the response curve’s shape. Our goal is to develop a nonparametric model for treatment response curves that achieves competitive performance against parametric models while allowing patient-specific customizations.