Hossein Estiri

This study develops a high-throughput phenotyping method using temporal sequences from EHR data, achieving superior classification performance compared to conventional methods.

This paper develops a clustering-based method to detect implausible values in EHR data, showing it has high specificity and fewer false positives than conventional approaches.

This paper evaluates a semi-supervised encoding approach for outlier detection in clinical observation data from EHRs, finding that semi-supervised encoders outperform conventional autoencoders.