66: Supervised Matrix Factorization for Estimating Individualized Treatment Rule

Music City Center 

Precision medicine aims to tailor treatments to the unique characteristics of individual patients. In this paper, we develop a classification-based approach to an estimate individualized treatment rule (ITR) by leveraging both structured quantitative data and high-dimensional unstructured textual documents. To tackle the challenge of incorporating text data, we propose an outcome-driven supervised nonnegative matrix factorization method that extracts relevant topics for ITR estimation in a one-step procedure. Our proposed method factorizes vectorized documents into a document-topic matrix and a topic-word matrix, guided by the outcome. For estimation, we constructed a weighted and penalized objective function, solved by a projected gradient approach, to jointly estimate the document representation and the ITR. Our formulation enables the interpretability of the effect of the learned topics on the ITR. We demonstrate the performance of our method through simulation studies and a real-world example from the MIMIC-IV intensive care unit dataset.

electronic health record

individualized treatment rule

natural language processing

topic modeling

precision medicine