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Bagging and deep learning in optimal individualized treatment rules. (English) Zbl 1436.62609

Summary: An ENsemble Deep Learning Optimal Treatment (EndLot) approach is proposed for personalized medicine problems. The statistical framework of the proposed method is based on the outcome weighted learning (OWL) framework which transforms the optimal decision rule problem into a weighted classification problem. We further employ an ensemble of deep neural networks (DNNs) to learn the optimal decision rule. Utilizing the flexibility of DNNs and the stability of bootstrap aggregation, the proposed method achieves a considerable improvement over existing methods. An R package “ITRlearn” is developed to implement the proposed method. Numerical performance is demonstrated via simulation studies and a real data analysis of the Cancer Cell Line Encyclopedia data.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62M45 Neural nets and related approaches to inference from stochastic processes

Software:

ITRLearn; deepTL; R
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