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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.07206 |
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| _version_ | 1866912185933889536 |
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| author | Mota, Marco Barbero Still, John M. Gamboa, Jorge L. Strobl, Eric V. Stein, Charles M. Kawai, Vivian K. Lasko, Thomas A. |
| author_facet | Mota, Marco Barbero Still, John M. Gamboa, Jorge L. Strobl, Eric V. Stein, Charles M. Kawai, Vivian K. Lasko, Thomas A. |
| contents | Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discover probabilistic independent sources from multimodal imperfect EHR data. These sources represent exogenous variables in the data generation process causal graph that estimate latent root causes of the presence of SLE in the health record. We objectively evaluated the sources against the original variables from which they were discovered by training supervised models to discriminate SLE from negative health records using a reduced set of labelled instances. We found 19 predictive sources with high clinical validity and whose EHR signatures define independent factors of SLE heterogeneity. Using the sources as input patient data representation enables models to provide with rich explanations that better capture the clinical reasons why a particular record is (not) an SLE case. Providers may be willing to trade patient-level interpretability for discrimination especially in challenging cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records Mota, Marco Barbero Still, John M. Gamboa, Jorge L. Strobl, Eric V. Stein, Charles M. Kawai, Vivian K. Lasko, Thomas A. Machine Learning Applications Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discover probabilistic independent sources from multimodal imperfect EHR data. These sources represent exogenous variables in the data generation process causal graph that estimate latent root causes of the presence of SLE in the health record. We objectively evaluated the sources against the original variables from which they were discovered by training supervised models to discriminate SLE from negative health records using a reduced set of labelled instances. We found 19 predictive sources with high clinical validity and whose EHR signatures define independent factors of SLE heterogeneity. Using the sources as input patient data representation enables models to provide with rich explanations that better capture the clinical reasons why a particular record is (not) an SLE case. Providers may be willing to trade patient-level interpretability for discrimination especially in challenging cases. |
| title | A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2501.07206 |