Improving the Usability of Donor Data from Registries Using an Extreme Learning Machine Approach to Upgrade Low-/Mid- to High-Resolution HLA Data

Background/Objectives: High-resolution Human Leukocyte Antigen (HLA) typing is required for unrelated donor selection, yet many registry records remain low- or intermediate-resolution. Haplotype frequency imputation is the standard remedy, but its accuracy decreases for rare haplotypes. Methods: A locus-specific Extreme Learning Machine (ELM) framework, trained on the Greek national donor registry (n = 117,345), upgrades low-/intermediate-resolution genotypes and imputes untyped loci; output weights are solved analytically. It was validated on two independent Greek cohorts (n = 20,100; 4353) against GRIMM and two expectation maximization baselines, on the same donors. Results: Accuracy depended on the high-resolution context available (94% for HLA-A with four anchor loci; 19–22% with none) and on the posterior probability threshold (78–91% ORAM, 70–84% GRPT), while call rate fell from 98.1% to ~41%; the two cannot be maximized together. GRIMM called fewer donors (1.8–47.9%) at comparable or higher accuracy. Allele-vocabulary abridgment excluded 14–47% of external-cohort allele calls; stratified by vocabulary membership, ORAM accuracy was 94.2% for in-vocabulary donors and 73.7% for the rest. The training procedure has no convergence guarantee. Conclusions: ELM upgrading converts legacy registry data into probabilistic high-resolution calls, with registry-scale training completing in minutes. These calls can narrow donor searches but cannot substitute for confirmatory typing.