Fabian Galis

Introduction: Therapeutic plasma exchange (TPE) is increasingly used as an adjunctive intervention in severe, hyperinflammatory critical illness, including COVID-19, yet clinical guidance remains syndromic and evidence is heterogeneous. We present an integrated, interpretable machine-learning framework designed to support protocolizable TPE decision-making by identifying biochemical phenotypes associated with short-horizon laboratory response to TPE. Our dataset consists of real-world intensive care unit cases and captures the treatment heterogeneity and operational constraints that a workable institutional protocol must accommodate, being well-suited as a “protocol seed” for iterative validation. Methods: We jointly analyze a COVID-19 cohort and a non-COVID comparator cohort receiving TPE. Three decision trees were constructed to represent: (1) global biochemical improvement, (2) strict improvement dependent on key inflammatory/coagulation markers, and (3) early interleukin-6 (IL-6) response. The models revealed distinct favorable phenotypes—particularly patients with IL-6 > 86 pg/mL, lactate dehydrogenase (LDH) >346 U/L, lymphopenia, and fibrinogen ≤ 8.3 g/L. Apriori analysis further identified high-confidence patterns linking high values of IL-6 and LDH with TPE responsiveness. We constructed a unified four-tier clinical algorithm for candidate stratification. Results: The resulting logic aligns with how TPE has been applied in practice in published severe COVID-19 series and trials, where candidate selection typically targets cytokine release syndrome-like phenotypes, organ dysfunction, and hyperinflammatory biomarker profiles. We further contextualize these findings against American Society for Apheresis guidance and COVID-era operational considerations, demonstrating convergence while adding quantitative biomarker thresholds. Conclusion: These findings support two complementary perspectives of benefit: (i) an algorithmic framework that provides transparent, protocol-ready guidelines, and (ii) a phenotype-based clinical approach consistent with observed post-TPE marker changes in severe COVID-19 cases. Larger multicenter validation is warranted; however, the present work provides a practical foundation for protocol construction and auditable decision support in settings already performing TPE.

In the context of unsupervised learning, effective clustering plays a vital role in revealing patterns and insights from unlabeled data. However, the success of clustering algorithms often depends on the relevance and contribution of features, which can differ between various datasets. This paper explores feature weighting for clustering and presents new weighting strategies, including methods based on SHAP (SHapley Additive exPlanations), a technique commonly used for providing explainability in various supervised machine learning tasks. By taking advantage of SHAP values in a way other than just to gain explainability, we use them to weight features and ultimately improve the clustering process itself in unsupervised scenarios. Our empirical evaluations across five benchmark datasets and clustering methods demonstrate that feature weighting based on SHAP can enhance unsupervised clustering quality, achieving up to a 22.69% improvement over other weighting methods (from 0.586 to 0.719 in terms of the Adjusted Rand Index). Additionally, these situations where the weighted data boosts the results are highlighted and thoroughly explored, offering insight for practical applications.