{"ID":2872653,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.08679","arxiv_id":"2509.08679","title":"Signal Fidelity Index-Aware Calibration for Dementia Predictions Across Heterogeneous Real-World Data","abstract":"\\textbf{Background:} Machine learning models trained on electronic health records (EHRs) often degrade across healthcare systems due to distributional shift. A fundamental but underexplored factor is diagnostic signal decay: variability in diagnostic quality and consistency across institutions, which affects the reliability of codes used for training and prediction. \\textbf{Objective:} To develop a Signal Fidelity Index (SFI) quantifying diagnostic data quality at the patient level in dementia, and to test SFI-aware calibration for improving model performance across heterogeneous datasets without outcome labels. \\textbf{Methods:} We built a simulation framework generating 2,500 synthetic datasets, each with 1,000 patients and realistic demographics, encounters, and coding patterns based on dementia risk factors. The SFI was derived from six interpretable components: diagnostic specificity, temporal consistency, entropy, contextual concordance, medication alignment, and trajectory stability. SFI-aware calibration applied a multiplicative adjustment, optimized across 50 simulation batches. \\textbf{Results:} At the optimal parameter ($α$ = 2.0), SFI-aware calibration significantly improved all metrics (p $\u003c$ 0.001). Gains ranged from 10.3\\% for Balanced Accuracy to 32.5\\% for Recall, with notable increases in Precision (31.9\\%) and F1-score (26.1\\%). Performance approached reference standards, with F1-score and Recall within 1\\% and Balanced Accuracy and Detection Rate improved by 52.3\\% and 41.1\\%, respectively. \\textbf{Conclusions:} Diagnostic signal decay is a tractable barrier to model generalization. SFI-aware calibration provides a practical, label-free strategy to enhance prediction across healthcare contexts, particularly for large-scale administrative datasets lacking outcome labels.","short_abstract":"\\textbf{Background:} Machine learning models trained on electronic health records (EHRs) often degrade across healthcare systems due to distributional shift. A fundamental but underexplored factor is diagnostic signal decay: variability in diagnostic quality and consistency across institutions, which affects the reliab...","url_abs":"https://arxiv.org/abs/2509.08679","url_pdf":"https://arxiv.org/pdf/2509.08679v1","authors":"[\"Jingya Cheng\",\"Jiazi Tian\",\"Federica Spoto\",\"Alaleh Azhir\",\"Daniel Mork\",\"Hossein Estiri\"]","published":"2025-09-10T15:19:04Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}