Missing predictor data increases the minimum sample size needed for stable, well-calibrated clinical prediction models, with context-specific inflation factors up to 2x, via adaptations to posterior sampling calculations.
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A fine-tuned deep learning model using systemic EHR data achieved AUROC 0.883 and PPV 0.657 for identifying glaucoma in a held-out Stanford cohort of over 20,000 patients.
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Incorporating Missing Data Considerations into Sample Size Calculations for Developing Clinical Prediction Models
Missing predictor data increases the minimum sample size needed for stable, well-calibrated clinical prediction models, with context-specific inflation factors up to 2x, via adaptations to posterior sampling calculations.
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Validating a Deep Learning Algorithm to Identify Patients with Glaucoma using Systemic Electronic Health Records
A fine-tuned deep learning model using systemic EHR data achieved AUROC 0.883 and PPV 0.657 for identifying glaucoma in a held-out Stanford cohort of over 20,000 patients.