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With the introduction of the Electric Health Records, large amounts ofdigital data become available for analysis and decision support. Whenphysicians are prescribing treatments to a patient, they need to consider alarge range of data variety and volume, making decisions increasingly complex.Machine learning based Clinical Decision Support systems can be a solution tothe data challenges. In this work we focus on a class of decision support inwhich the physicians' decision is directly predicted. Concretely, the modelwould assign higher probabilities to decisions that it presumes the physicianare more likely to make. Thus the CDS system can provide physicians withrational recommendations. We also address the problem of correlation in targetfeatures: Often a physician is required to make multiple (sub-)decisions in ablock, and that these decisions are mutually dependent. We propose a solutionto the target correlation problem using a tensor factorization model. In orderto handle the patients' historical information as sequential data, we apply theso-called Encoder-Decoder-Framework which is based on Recurrent Neural Networks(RNN) as encoders and a tensor factorization model as a decoder, a combinationwhich is novel in machine learning. With experiments with real-world datasetswe show that the proposed model does achieve better prediction performances.

Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding