Author

Christopher Barton

University of California, San Francisco

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

Biology Disciplines

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Bioinformatics· 4

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 4 Research Focus:benchmarking· 3 Assay:electronic health records· 3 Research Focus:drug response / drug discovery· 1 Organism:human· 1

Frequent Co-Authors

Ritankar DasiD· 4 shared Jacob S. CalvertiD· 3 shared Lisa Y. ShiehiD· 3 shared Uli K. ChettipallyiD· 3 shared Anna Lynn-Palevsky· 2 shared David W. ShimabukuroiD· 2 shared Grant Fletcher· 2 shared Hamid Mohamadlou· 2 shared

Activity by Year

2017-2019 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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4 shared papersBioinformatics

Ritankar DasORCID

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4 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

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, Jacob S. Calvert, Christopher Barton, David W. Shimabukuro, Lisa Y. Shieh, Uli K. Chettipally, Grant Fletcher, Yaniv Kerem, Yifan Zhou, Ritankar Das

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

BMJ Open · Jan 1, 2018Peer-reviewedBioinformatics

Pediatric Severe Sepsis Prediction Using Machine Learning

Thomas Desautels, Jana L. Hoffman, Christopher Barton, Qingqing Mao, Melissa Jay, Jacob S. Calvert, Ritankar Das

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.

bioRxiv · Nov 22, 2017 · PreprintBioinformatics