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Publications

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

Diagnostics

Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients

Calvert J, Saber N, Hoffman 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

BMJ Open

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, 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

Journal of Medical Economics

Cost and mortality impact of an algorithm-driven sepsis prediction system

Calvert J, Hoffman J, Barton C, Shimabukuro D, Ries M, Chettipally U, Kerem Y, Jay M, Mataraso S, 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

Journal of the American Medical Informatics Association

Discharge recommendation based on a novel technique of homeostatic analysis

Calvert J, Price D, Barton C, Chettipally U, Das R