Dual-level diagnostic feature learning with recurrent neural networks for treatment sequence recommendation

In recent years, the massive electronic medical records (EMRs) have supported the development of intelligent medical services such as treatment recommendations. However, existing treatment recommendations usually follow the traditional sequential recommendation strategies, ignoring the partial temporality of the practical process and the patient's diagnostic features. To this end, in this paper, we propose a new Dual-level Diagnostic Feature Learning with Recurrent Neural Network for treatment sequence recommendation (DDFL-RNN), where the dual-level diagnostic features including patients' historical medical records and current treatment results. Firstly, we divide the dataset into several sequential sets of treatment item based on the patient's treatment days. Secondly, we propose two kinds of attention mechanisms to learn diagnostic features, including the elemental attention mechanism and the sequential attention mechanism. Finally, the dual-level learned diagnostic features are brought into the recurrent neural network for encoding and recommendation. Extensive experiments on a breast cancer dataset from a first-rate hospital have shown that our model achieves significantly better performance than several classical and state-of-the-art baseline methods.