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Algorithm scours electronic health records to reveal hidden kidney disease


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NEW YORK, NY Diagnosing chronic kidney disease, which is often undetected until it causes irreversible damage, may soon become automated with a new algorithm that interprets data from electronic medical records.
The algorithm, developed by researchers at Columbia University Vagelos College of Physicians and Surgeons, automatically scours a patient s electronic medical record for results of blood and urine tests and, using a mix of established equations and machine learning to process the data, can alert physicians to patients in the earliest stages of chronic kidney disease.
A study of the algorithm was published in the journal 
npj Digital Medicine in April. ....

New York , United States , Mailman School Of Public Health , South Australia , Chunhua Weng , Elizabethw Karlson , Iuliana Ionita Laza , Krzysztof Kiryluk , Atlas Khan , Paule Drawz , Richarda Dart , Karla Mehl , George Hripcsak , Sumit Mohan , David Fasel , Fernanda Polubriaginof , Alig Gharavi , Peggyl Peissig , Nicholasp Tatonetti , Ericb Larson , Matthewa Hathcock Mayo , Francesca Zanoni , Shefali Setia Verma , Columbia University Irving Medical Center , Vagelos College Of Physicians , National Institute Of Diabetes ,

New statistical method exponentially increases ability to discover genetic insights


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Pleiotropy analysis, which provides insight on how individual genes result in multiple characteristics, has become increasingly valuable as medicine continues to lean into mining genetics to inform disease treatments. Privacy stipulations, though, make it difficult to perform comprehensive pleiotropy analysis because individual patient data often can t be easily and regularly shared between sites. However, a statistical method called Sum-Share, developed at Penn Medicine, can pull summary information from many different sites to generate significant insights. In a test of the method, published in
Nature Communications, Sum-Share s developers were able to detect more than 1,700 DNA-level variations that could be associated with five different cardiovascular conditions. If patient-specific information from just one site had been used, as is the norm now, only one variation would have been determined. ....

Rui Duan , Hakon Hakonarson , Marylynd Ritchie , Yong Chen , Robert Carroll , Sarah Pendergrass , Christopher Bauer , Joshc Denny , Ruowang Li , Jonathand Mosley , Patrick Sleiman , Wei Qi , Jason Moore , Thomas Lumley , Davids Carrell , Dignar Velez Edwards , Xinyuan Zhang , Institute For Biomedical Informatics , Nature Communications , National Institutes Of Health , Consortium For Clinical Characterization , Penn Medicine , Biomedical Informatics , Clinical Characterization , National Institutes , Georgia Wiesner ,