Cost-Aware Distillation of a Commercial First-Trimester Preeclampsia Screening Engine in Northern Vietnam: Tiered Feature Sets and Explain Ability - Thao, Ta Van; Phuong, Vu Thi Minh; Huong, Nguyen Lien; Tam, Duong Thanh; Viet, Tran Sach; Bac, Vu Ngoc; Anh, Vu Ngoc (Tạp chí Nghiên cứu Y học, 2026)
Commercial first-trimester preeclampsia (PE) screening engines (e.g., PerkinElmer/FMF) integrate maternal factors, biophysical measures, Doppler indices, and biochemical markers to generate continuous risk ratios, but their use in resource-constrained settings is limited by missing data, centralized assays, and limited transparency. We developed a tiered, interpretable machine-learning (ML) distillation pipeline to approximate the PerkinElmer risk ratio for PE <37 weeks, evaluate fidelity across cost-aware feature tiers, identify minimal deployable feature sets, and assess agreement at the clinical cut-off. A retrospective cohort of 1,051 singleton pregnancies from Northern Vietnam (2023–2025) was split into training (n=850) and test (n=201) sets. Complete-case tiers were defined as Tier 0 (maternal factors + MAP), Tier 1 (+ PlGF/PAPP-A), and Tier 2 (+ UtA-PI). Fidelity was assessed using ranking, regression, and calibration metrics, with interpretability via permutation importance, SHAP, and ablation, and threshold mimicry using the PerkinElmer cut-off. Despite declining tier availability, fidelity remained high (Spearman ρ=0.975–0.981), with strong top-rank agreement and excellent AUC-ROC (0.97–1.00). A minimal feature set (MAP MoM, BMI, parity, PlGF MoM, UtA-PI MoM) retained ≥95% fidelity, supporting scalable, explainable PE triage under real-world constraints.