Researchers have developed an algorithm to address unknown missingness in sparse electronic health records (EHRs), which is crucial for improving data accuracy and machine learning applications in healthcare. This transformer-based neural network outperforms current imputation methods by accurately recovering missing binary values, leading to better performance in predicting hospital readmissions from EHR data.
Read the full article at arXiv cs.LG (ML)
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Ali NematiWritten by Ali
