Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.

Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy. Phys Med Biol. 2019 Nov 07;: Authors: Wang R, Weng Y, Zhou Z, Chen L, Hao H, Wang J Abstract Accurately predicting treatment outcome is crucial for creating personalized treatment plans and follow-up schedules. Electronic health records (EHRs) contain valuable patient-specific information that can be leveraged to improve outcome prediction. We propose a reliable multi-objective ensemble deep learning (MoEDL) method that uses features extracted from EHRs to predict high risk of treatment failure after radiotherapy in patients with lung cancer. The dataset used in this study contains EHRs of 814 patients who had not achieved disease-free status and 193 patients who were disease-free with at least one year follow-up time after lung cancer radiation therapy. The proposed MoEDL consists of three phases: 1) training with dynamic ensemble deep learning; 2) model selection with adaptive multi-objective optimization; and 3) testing with evidential reasoning (ER) fusion. Specifically, in the training phase, we employ deep perceptron networks as base learners to handle various issues with EHR data. The architecture and key hyper-parameters of each base learner are dynamically adjusted to increase the diversity of learners while reducing the time spent tuning hyper-parameters. Furthermore, we integrate the Snapshot Ensembles...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research