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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2507.19530 |
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| _version_ | 1866914407516209152 |
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| author | Azam, Md Basit Singh, Sarangthem Ibotombi |
| author_facet | Azam, Md Basit Singh, Sarangthem Ibotombi |
| contents | External validation remains rare in healthcare machine learning despite being critical for establishing real-world feasibility. We developed an ensemble framework to predict blood pressure from electronic health records, incorporating rigorous data leakage prevention. Internal validation on the MIMIC-III dataset yielded moderate performance for systolic (R^2 = 0.248, RMSE = 14.84 mmHg) and diastolic (R^2 = 0.297, RMSE = 8.27 mmHg) blood pressure. However, external validation on the eICU dataset revealed substantial generalization challenges. Baseline systolic performance dropped significantly from R^2 = 0.248 to -0.024, with RMSE increasing from 14.84 to 18.69 mmHg. To address potential confounding from feature imputation, we conducted an intersection-only experiment using 16 universally available features; this yielded worse external performance (R^2 = -0.115, RMSE = 17.32 mmHg), proving imputation artifacts were not the primary cause. Attempts at post-hoc correction, including linear and isotonic recalibration (R^2 ranging from -0.170 to 0.024) and domain adaptation via covariate shift reweighting (R^2 = -0.141), showed limited gains. This highlights fundamental cross-institutional barriers. Our root-cause analysis identified three primary obstacles to generalizability: (1) site-specific feature distributions, even among standard physiological variables; (2) underlying patient population differences with unique pathophysiologies; and (3) institutional variations in measurement protocols creating non-transferable learned patterns. These findings demonstrate that strong internal performance cannot guarantee cross-institutional deployment success. Transparent reporting of validation failures is essential for setting realistic expectations for predictive models. Code is available at https://github.com/mdbasit897/ehr-bp-ensemble. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Validation Fails: Cross-Institutional Blood Pressure Prediction and the Limits of Electronic Health Record-Based Models Azam, Md Basit Singh, Sarangthem Ibotombi Machine Learning Artificial Intelligence External validation remains rare in healthcare machine learning despite being critical for establishing real-world feasibility. We developed an ensemble framework to predict blood pressure from electronic health records, incorporating rigorous data leakage prevention. Internal validation on the MIMIC-III dataset yielded moderate performance for systolic (R^2 = 0.248, RMSE = 14.84 mmHg) and diastolic (R^2 = 0.297, RMSE = 8.27 mmHg) blood pressure. However, external validation on the eICU dataset revealed substantial generalization challenges. Baseline systolic performance dropped significantly from R^2 = 0.248 to -0.024, with RMSE increasing from 14.84 to 18.69 mmHg. To address potential confounding from feature imputation, we conducted an intersection-only experiment using 16 universally available features; this yielded worse external performance (R^2 = -0.115, RMSE = 17.32 mmHg), proving imputation artifacts were not the primary cause. Attempts at post-hoc correction, including linear and isotonic recalibration (R^2 ranging from -0.170 to 0.024) and domain adaptation via covariate shift reweighting (R^2 = -0.141), showed limited gains. This highlights fundamental cross-institutional barriers. Our root-cause analysis identified three primary obstacles to generalizability: (1) site-specific feature distributions, even among standard physiological variables; (2) underlying patient population differences with unique pathophysiologies; and (3) institutional variations in measurement protocols creating non-transferable learned patterns. These findings demonstrate that strong internal performance cannot guarantee cross-institutional deployment success. Transparent reporting of validation failures is essential for setting realistic expectations for predictive models. Code is available at https://github.com/mdbasit897/ehr-bp-ensemble. |
| title | When Validation Fails: Cross-Institutional Blood Pressure Prediction and the Limits of Electronic Health Record-Based Models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.19530 |