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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/2512.14272 |
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| _version_ | 1866908714626187264 |
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| author | Buckley, Brian O'Hagan, Adrian Galligan, Marie |
| author_facet | Buckley, Brian O'Hagan, Adrian Galligan, Marie |
| contents | The VBphenoR package for R provides a closed-form variational Bayes approach to patient phenotyping using Electronic Health Records (EHR) data. We implement a variational Bayes Gaussian Mixture Model (GMM) algorithm using closed-form coordinate ascent variational inference (CAVI) to determine the patient phenotype latent class. We then implement a variational Bayes logistic regression, where we determine the probability of the phenotype in the supplied EHR cohort, the shift in biomarkers for patients with the phenotype of interest versus a healthy population and evaluate predictive performance of binary indicator clinical codes and medication codes. The logistic model likelihood applies the latent class from the GMM step to inform the conditional. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14272 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A variational Bayes latent class approach for EHR-based patient phenotyping in R Buckley, Brian O'Hagan, Adrian Galligan, Marie Computation Machine Learning The VBphenoR package for R provides a closed-form variational Bayes approach to patient phenotyping using Electronic Health Records (EHR) data. We implement a variational Bayes Gaussian Mixture Model (GMM) algorithm using closed-form coordinate ascent variational inference (CAVI) to determine the patient phenotype latent class. We then implement a variational Bayes logistic regression, where we determine the probability of the phenotype in the supplied EHR cohort, the shift in biomarkers for patients with the phenotype of interest versus a healthy population and evaluate predictive performance of binary indicator clinical codes and medication codes. The logistic model likelihood applies the latent class from the GMM step to inform the conditional. |
| title | A variational Bayes latent class approach for EHR-based patient phenotyping in R |
| topic | Computation Machine Learning |
| url | https://arxiv.org/abs/2512.14272 |