Leveraging Ensemble Machine Learning Models for the Detection of Primary Myelofibrosis in Electronic Health Records

Background and Objectives: Primary myelofibrosis (PMF) is a rare hematologic malignancy with non-specific symptoms, causing diagnostic delays and missed diagnoses. Automated screening in heterogeneous electronic health records (EHRs) is challenging due to class imbalance, data sparsity, and incomplete labeling. We investigated two complementary objectives: (1) developing a screening algorithm using routine EHR data to identify PMF-risk patients for hematology consultation, and (2) assessing the applicability of positive-unlabeled (PU) learning. Methods: Using EHR data from 10 Polish hospitals, we evaluated several ensemble models and found that LightGBM achieved the best performance. Results: LightGBM achieved AP 20.83% (95% CI: 19.18–24.35%), sensitivity 45.52% (95% CI: 39.48–52.72%), and precision 14.72% (95% CI: 13.80–17.01%)—substantially exceeding expected prevalence (over 340-fold enrichment). The model captured interactions among RDW and PLT parameters revealing high-risk unlabeled patients with confirmed diagnoses (n = 5) or clinical profiles resembling PMF cases (n = 31), confirming PU problem. Conclusions: Although PU methods enhanced sensitivity, they reduced precision to levels exceeding real-world healthcare capacity. Ensemble learning enables disease identification in clinical settings.