Application of convex hull analysis for the evaluation of data heterogeneity between patient populations of different origin and implications of hospital bias in downstream machine-learning-based data processing: A comparison of 4 critical-care patient datasets - Sharafutdinov, Konstantin; Bhat, Jayesh S.; Fritsch, Sebastian Johannes; Nikulina, Kateryna; E. Samadi, Moein; Polzin, Richard; Mayer, Hannah; Marx, Gernot; Bickenbach, Johannes; Schuppert, Andreas (Frontiers in Big Data, 2022)
As ML models exhibit poor predictive performance over data ranges sparsely or not covered by the learning dataset, in this paper we propose a novel method to assess their generalization capability among different hospitals based on the convex hull (CH) overlap between multivariate datasets. To reduce dimensionality effects, we used a two-step approach. First, CH analysis was applied to find mean CH coverage between each of two data sets, resulting in a upper bound of prediction range. Second, 4 types of ML models were trained to classify the origin of a dataset (i.e. from which hospital) and to estimate differences in datasets with respect to underlying distributions. To demonstrate the applicability of our method, we used 4 critical-care patient datasets from different hospitals in Germany and USA. We estimated the similarity of these populations and investigated whether ML models developed on one dataset can be reliably applied to another one. We show that the strongest drop in performance was associated with the poor intersection of convex hulls in the corresponding hospitals’ datasets and with high performance of ML methods for dataset discrimination. Hence, we suggest the application of our pipeline as a first tool to assess the transferability of trained models. We emphasize that datasets from different hospitals represent heterogeneous data sources, and the transfer from one database to another should be performed with utmost care to avoid implications during real-world applications of the developed models. Further research is needed to develop methods for the adaptation of ML models to new hospitals. In addition, more work should be aimed at the creation of gold standard datasets that are large and diverse with data from varied application sites.