L
Lisa Shieh
Researcher at Stanford University
Publications - 88
Citations - 8711
Lisa Shieh is an academic researcher from Stanford University. The author has contributed to research in topics: Medicine & Health care. The author has an hindex of 18, co-authored 74 publications receiving 6621 citations. Previous affiliations of Lisa Shieh include Massachusetts Institute of Technology.
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Journal ArticleDOI
Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016
Andrew Rhodes,Laura Evans,Waleed Alhazzani,Mitchell M. Levy,Massimo Antonelli,Ricard Ferrer,Anand Kumar,Jonathan E. Sevransky,Charles L. Sprung,Mark E. Nunnally,Bram Rochwerg,Gordon D. Rubenfeld,Derek C. Angus,Djillali Annane,Richard Beale,Geoffrey J. Bellinghan,Gordon R. Bernard,Jean Daniel Chiche,Craig M. Coopersmith,Daniel De Backer,Craig French,Seitaro Fujishima,Herwig Gerlach,Jorge Hidalgo,Steven M. Hollenberg,Alan E. Jones,Dilip R. Karnad,Ruth M. Kleinpell,Younsuck Koh,Thiago Lisboa,Flávia Ribeiro Machado,John J. Marini,John C. Marshall,John E. Mazuski,Lauralyn McIntyre,Anthony S. McLean,Sangeeta Mehta,Rui Moreno,John Myburgh,Paolo Navalesi,Osamu Nishida,Tiffany M. Osborn,Anders Perner,Colleen M. Plunkett,Marco Ranieri,Christa A. Schorr,Maureen A. Seckel,Christopher W. Seymour,Lisa Shieh,Khalid A. Shukri,Steven Q. Simpson,Mervyn Singer,B. Taylor Thompson,Sean R. Townsend,Thomas Van der Poll,Jean Louis Vincent,W. Joost Wiersinga,Janice L. Zimmerman,R. Phillip Dellinger +58 more
TL;DR: Although a significant number of aspects of care have relatively weak support, evidence-based recommendations regarding the acute management of sepsis and septic shock are the foundation of improved outcomes for these critically ill patients with high mortality.
Journal ArticleDOI
Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016.
Andrew Rhodes,Laura Evans,Waleed Alhazzani,Mitchell M. Levy,Massimo Antonelli,Ricard Ferrer,Anand Kumar,Jonathan E. Sevransky,Charles L. Sprung,Mark E. Nunnally,Bram Rochwerg,Gordon D. Rubenfeld,Derek C. Angus,Djillali Annane,Richard Beale,Geoffrey J. Bellinghan,Gordon R. Bernard,Jean Daniel Chiche,Craig M. Coopersmith,Daniel De Backer,Craig French,Seitaro Fujishima,Herwig Gerlach,Jorge Hidalgo,Steven M. Hollenberg,Alan E. Jones,Dilip R. Karnad,Ruth M. Kleinpell,Younsuck Koh,Thiago Lisboa,Flávia Ribeiro Machado,John J. Marini,John C. Marshall,John E. Mazuski,Lauralyn McIntyre,Anthony S. McLean,Sangeeta Mehta,Rui Moreno,John Myburgh,Paolo Navalesi,Osamu Nishida,Tiffany M. Osborn,Anders Perner,Colleen M. Plunkett,Marco Ranieri,Christa A. Schorr,Maureen A. Seckel,Christopher W. Seymour,Lisa Shieh,Khalid A. Shukri,Steven Q. Simpson,Mervyn Singer,B. Taylor Thompson,Sean R. Townsend,Thomas Van der Poll,Jean Louis Vincent,W. Joost Wiersinga,Janice L. Zimmerman,R. Phillip Dellinger +58 more
TL;DR: A consensus committee of 55 international experts representing 25 international organizations was assembled at key international meetings (forSurviving Sepsis Campaign Guidelines for Management of Sepsis and Septic Shock: 2012 as discussed by the authors ).
Journal ArticleDOI
Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach.
Thomas Desautels,Jacob Calvert,Jana Hoffman,Melissa Jay,Yaniv Kerem,Lisa Shieh,David Shimabukuro,Uli K. Chettipally,Feldman,Christopher Barton,David J. Wales,Ritankar Das +11 more
TL;DR: InSight, a machine learning classification system that uses multivariable combinations of easily obtained patient data, is an effective tool for predicting sepsis onset and performs well even with randomly missing data.
Journal ArticleDOI
Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU
Qingqing Mao,Melissa Jay,Jana Hoffman,Jacob Calvert,Christopher Barton,David Shimabukuro,Lisa Shieh,Uli K. Chettipally,Grant S. Fletcher,Yaniv Kerem,Yifan Zhou,Ritankar Das +11 more
TL;DR: InSight is robust to missing data, can be customised to novel hospital data using a small fraction of site data and retains strong discrimination across all institutions.
Journal ArticleDOI
Restrictive blood transfusion practices are associated with improved patient outcomes.
Lawrence T. Goodnough,Paul M. Maggio,Eric Hadhazy,Lisa Shieh,Tina Hernandez-Boussard,Paul Khari,Neil P. Shah +6 more
TL;DR: The impact of clinical decision support at computerized physician order entry and education on red blood cell (RBC) transfusions and clinical patient outcomes at an institution is assessed.