Ritankar Das

This paper validates a machine learning algorithm for predicting 48-hour all-cause mortality across multiple centers, showing high AUROC values compared to qSOFA and MEWS.

This paper evaluates a machine learning algorithm for predicting severe sepsis and shows it reduces mortality, length of stay, and readmission rates in real-world clinical use.

This study develops a machine learning model to predict acute kidney injury using electronic health records, achieving higher accuracy than traditional clinical scores.

This paper validates a sepsis prediction algorithm using vital sign data across multiple settings, showing strong performance and generalizability.

This study develops a machine learning algorithm using EHR data to predict pediatric severe sepsis onset, achieving high AUROC scores and outperforming existing clinical scores.