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4 results on '"Pellegrini, Emily"'

1. Additional file 1 of Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data

Author

Ashwath Radhachandran, Anurag Garikipati, Zelin, Nicole S., Pellegrini, Emily, Ghandian, Sina, Calvert, Jacob, Hoffman, Jana, Qingqing Mao, and Ritankar Das

Abstract

Additional file 1: Fig. S1. Machine learning workflow. Table S1. Inputs used to train the machine learning algorithm. Table S2. International Classification of Diseases, Tenth Revision (ICD-10) codes used to identify emergency department encounters for acute heart failure. Table S3. Hyperparameter Optimization for the Top5F and 33F models. For each model, the first row lists the values of the optimal hyperparameters selected during model training. For each subsequent row, a single (bolded) hyperparameter is altered, and all other hyperparameters are unchanged.

Published
2021
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2. Supplemental Material, sj-pdf-1-cat-10.1177_1076029621991185 - A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients

Author

Ryan, Logan, Mataraso, Samson, Siefkas, Anna, Pellegrini, Emily, Barnes, Gina, Green-Saxena, Abigail, Hoffman, Jana, Calvert, Jacob, and Ritankar Das

Subjects
body regions, FOS: Clinical medicine, Cardiology, 111599 Pharmacology and Pharmaceutical Sciences not elsewhere classified
Abstract

Supplemental Material, sj-pdf-1-cat-10.1177_1076029621991185 for A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients by Logan Ryan, Samson Mataraso, Anna Siefkas, Emily Pellegrini, Gina Barnes, Abigail Green-Saxena, Jana Hoffman, Jacob Calvert and Ritankar Das in Clinical and Applied Thrombosis/Hemostasis

Published
2021
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3. Additional file 1 of Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

Author

Burdick, Hoyt, Pino, Eduardo, Gabel-Comeau, Denise, Gu, Carol, Roberts, Jonathan, Le, Sidney, Slote, Joseph, Saber, Nicholas, Pellegrini, Emily, Green-Saxena, Abigail, Hoffman, Jana, and Ritankar Das

Subjects
nervous system, musculoskeletal, neural, and ocular physiology, macromolecular substances
Abstract

Additional file 1. Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.

Published
2020
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4. Supplementary_Materials – Supplemental material for Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction

Author

Mohamadlou, Hamid, Saarang Panchavati, Calvert, Jacob, Lynn-Palevsky, Anna, Le, Sidney, Angier Allen, Pellegrini, Emily, Green-Saxena, Abigail, Barton, Christopher, Fletcher, Grant, Shieh, Lisa, Stark, Philip B, Chettipally, Uli, Shimabukuro, David, Feldman, Mitchell, and Ritankar Das

Subjects
FOS: Computer and information sciences, 111708 Health and Community Services, 89999 Information and Computing Sciences not elsewhere classified, FOS: Health sciences
Abstract

Supplemental material, Supplementary_Materials for Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction by Hamid Mohamadlou, Saarang Panchavati, Jacob Calvert, Anna Lynn-Palevsky, Sidney Le, Angier Allen, Emily Pellegrini, Abigail Green-Saxena, Christopher Barton, Grant Fletcher, Lisa Shieh, Philip B Stark, Uli Chettipally, David Shimabukuro, Mitchell Feldman and Ritankar Das in Health Informatics Journal

Published
2019
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