An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios

Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis.

Automated summary

A new machine learning model combining Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) was proposed for dynamic evolving diagnostic task scenarios, focusing on aggregating simple models more easily understood by physicians and requiring less training data. The model can choose the optimal number of layers and basic learners to achieve the best performance based on data distribution and task characteristics, outperforming baseline models on different datasets. The findings suggest important implications for the application of machine learning models in computer-aided diagnosis tasks with variable datasets and feature sets.