Author

Hamid Mohamadlou

Dascena (United States)

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:electronic health records· 2 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Anna Lynn-Palevsky· 2 shared Christopher Barton· 2 shared Lisa Y. ShiehiD· 2 shared Ritankar DasiD· 2 shared Uli K. ChettipallyiD· 2 shared Abigail Green‐SaxenaiD· 1 shared Angier O. AlleniD· 1 shared David W. ShimabukuroiD· 1 shared

Activity by Year

2018-2019 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Anna Lynn-Palevsky

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Multicenter validation of a machine learning algorithm for 48 hour all-cause mortality prediction

Hamid Mohamadlou, Saarang Panchavati, Jacob Calvert, Anna Lynn-Palevsky, Sidney Le, Angier O. Allen, Emily Pellegrini, Abigail Green‐Saxena, Christopher Barton, Grant Fletcher, Lisa Y. Shieh, Philip B. Stark, Uli K. Chettipally, David W. Shimabukuro, Mitchell D. Feldman, 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.

Health Informatics Journal · Dec 30, 2019Peer-reviewedBioinformatics

Prediction of Acute Kidney Injury with a Machine Learning Algorithm using Electronic Health Record Data

Hamid Mohamadlou, Anna Lynn-Palevsky, Christopher Barton, Uli K. Chettipally, Lisa Y. Shieh, Jacob S. Calvert, Nicholas Saber, Ritankar Das

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

Canadian Journal of Kidney Health and Disease · Jan 1, 2018Peer-reviewedBioinformatics