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Publications
Supported by leading scientific bodies
2021
Clinical and Applied Thrombosis/Hemostasis
A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients
Logan Ryan, BS, Samson Mataraso, BS, Anna Siefkas, SM, Emily Pellegrini, MEng, Gina Barnes, MPH, Abigail Green-Saxena, PhD, Jana Hoffman, PhD, Jacob Calvert, MSc, Ritankar Das, MSc
2020
Annals of Medicine and Surgery
Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study
Ryan L, Carson L, Mataraso S, Allen A, Green-Saxena A, Pellegrini E, Hoffman J, Barton C, McCoy A, Das, R
BMJ Health & Care Informatics
Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals
Burdick H, Pino E, Gabel-Comeau D, McCoy A, Gu C, Roberts J, Le S, Slote J, Pellegrini E, Green-Saxena A, Hoffman J, Das R
Computers in Biology and Medicine
Prediction of respiratory decomposition in Covid-19 patients using machine learning: The READY Trial
Burdick H, Lam C, Mataraso S, Siefkas A, Braden G, Dellinger R.P, McCoy A, Vincent J-L, Green-Saxena A, Hoffman J, Calvert J, Pellegrini E, Das R
Journal of Critical Care
Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS)
Le S, Pellegrini E, Green-Saxena A, Summers C, Hoffman J, Calvert J, Das R
JMIR Public Health & Surveillance
A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: An Algorithm Development Study
Allen A, Mataraso S, Siefkas A, Burdick H, Braden G, Dellinger R.P, McCoy A, Pelligrini E, Hoffman J, Green-Saxena A, Barnes G, Calvert J, Das R
BMC Medical Informatics & Decision Making
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
Hoyt B, Eduardo P, Gabel-Comeau D, Gu C, Roberts J, Le S, Slote J, Saber N, Pelligrini E, Green-Saxena A, Hoffman J, Das R
Journal of Critical Medicine
Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit From Hydroxychloroquine Treatment? - The IDENTIFY Trial
Burdick H, Lam C, Mataraso S, Siefkas A, Braden G, Dellinger R.P, McCoy A, Vincent J-L, Green-Saxena A, Barnes G, Hoffman J, Calvert J, Pelligrini E, Das R
2019
Health Informatics Journal
Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction
Mohamadlou H, Panchavati S, Calvert J, Lynn-Palevsky A, Le S, Allen A, Pellegrini E, Green-Saxena A,Barton C, Fletcher G, Shieh L, Stark P, Chettipally U, Shimabukuro D, Feldman M, Das R
Frontiers in Pediatrics
Pediatric Severe Sepsis Prediction Using Machine Learning
Le S, Hoffman J, Barton C, Fitzgerald J, Allen A, Pellegrini E, Calvert J, Das R
Computers in Biology and Medicine
Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
Barton C, Chettipally U, Zhou Y, Jiang Z, Lynn-Palevsky A, Le S, Calvert J, Das R
2018
Canadian Journal of Kidney Health and Disease
Prediction of acute kidney injury with a machine learning algorithm using electronic health record data
Mohamadlou H, Lynn-Palevsky A, Barton C, Chettipally U, Shieh L, Calvert J, Saber N, Das R
2017
BMJ Open Respiratory Research
Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial
Shimabukuro D, Barton C, Feldman M, Mataraso S, Das R
BMJ Open Quality
Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit, and hospital floor units
McCoy A, Das R
BMJ Open
Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach
Desautels T, Das R, Calvert J, Trivedi M, Summers C, Wales D, Ercole A
Royal Society Open Science
Machine learning landscapes and predictions for patient outcomes
Das R, Wales D
Biomedical Informatics Insights
Using transfer learning for improved mortality prediction in a data-scarce hospital setting
Desautels T, Calvert J, Hoffman J, Mao Q, Jay M, Fletcher G, Barton C, Chettipally U, Kerem Y, Das R
2016
JMIR Medical Informatics
Prediction of sepsis in the intensive care unit with minimal electronic health record data: A machine learning approach
Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman M, Barton C, Wales D, Das R
Annals of Medicine and Surgery
Using electronic health record collected clinical variables to predict medical intensive care unit mortality
Calvert J, Mao Q, Hoffman J, Jay M, Desautels T, Mohamadlou H, Chettipally U, Das R
Computers in Biology and Medicine
A computational approach to mortality prediction of alcohol use disorder inpatients
Calvert J, Mao Q, Rogers A, Barton C, Jay M, Desautels T, Mohamadlou H, Jan J, Das R
Computers in Biology and Medicine
A computational approach to early sepsis detection
Calvert J, Price D, Chettipally U, Barton CW, Feldman M, Hoffman J, Jay M, Das R
Annals of Medicine and Surgery
High-performance detection and early prediction of septic shock for alcohol-use disorder patients
Calvert J, Desautels T, Chettipally U, Barton C, Hoffman J, Jay M, Mao Q, Mohamadlou H, Das R