Author

Uli K. Chettipally

University of California, San FranciscoORCID 0000-0001-8833-5478Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 3 Research Focus:benchmarking· 2 Assay:electronic health records· 2 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Christopher Barton· 3 shared Lisa Y. ShiehiD· 3 shared Ritankar DasiD· 3 shared Anna Lynn-Palevsky· 2 shared David W. ShimabukuroiD· 2 shared Grant Fletcher· 2 shared Hamid Mohamadlou· 2 shared Jacob S. CalvertiD· 2 shared

Activity by Year

2018-2019 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 43 mutual co-author links

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3 shared papersBioinformatics

Christopher Barton

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3 papers

Recent·Most cited

BioinformaticsHealth Informatics JournalPeer-reviewedDec 30, 2019

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

Hamid Mohamadlou, Saarang Panchavati, Jacob Calvert…·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.

20 citations

electronic health recordsmachine learning (classical)

𝕏 PostBluesky

BioinformaticsCanadian Journal of Kidney Health and DiseasePeer-reviewedJan 1, 2018

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

Hamid Mohamadlou, Anna Lynn-Palevsky, Christopher Barton…·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.

159 citationstop 1%

electronic health recordsmachine learning (classical)

𝕏 PostBluesky

BioinformaticsBMJ OpenPeer-reviewedJan 1, 2018

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Qingqing Mao, Melissa Jay, Jana L. Hoffman…·Ritankar Das

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

336 citationstop 1%

machine learning (classical)benchmarking

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