A New Approach to Predict Lupus Flare Level Using Calibrated Ensemble
In response to the conflict between the essential assumption in machine learning algorithm that data points are all drawn from the same distribution and the data heterogeneity in real-world tasks, we introduced a new approach called Calibrated Ensemble targeting to improve the performance of classification model with diverse data sources. Instead of using the whole dataset to train individual models in regular ensemble methods, CE trains baseline learners by their calibrated subgroups. Clinically, this can be interpreted as several experts and each expert is proficient in diagnosing patients with some specific manifestations. Our model utilized and amplified the strength of various machine learning algorithms by moving data points to the subgroup given the best prediction performance in the training process. We implemented our model in predicting SLE flares using rSFI as our index by 1541 clinic encounters from MSH. The result from CE exceeded the performance of either individual algorithms or regular ensembles across seven performance evaluation metrics. Several technologies aimed to address imbalance and parameters chosen are applied to improve the models were applied. In addition, we analyze the instances in every cluster and found out the characteristic features in each cluster by ANOVA and Tukey HSD post-hoc test.
Computer science|Artificial intelligence
Qin, Man, "A New Approach to Predict Lupus Flare Level Using Calibrated Ensemble" (2021). ETD Collection for Fordham University. AAI28652184.