Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study
Authors
Keywords
-
Journal
JMIR Medical Informatics
Volume 8, Issue 7, Pages e15182
Publisher
JMIR Publications Inc.
Online
2020-01-01
DOI
10.2196/15182
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A Scalable Data Science Platform for Healthcare and Precision Medicine Research (Preprint)
- (2019) Jacob McPadden et al. JOURNAL OF MEDICAL INTERNET RESEARCH
- Early Administration of Antibiotics for Suspected Sepsis
- (2019) Michael Y. Mi et al. NEW ENGLAND JOURNAL OF MEDICINE
- Artificial Intelligence in Health Care
- (2019) Ezekiel J. Emanuel et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Clinical considerations when applying machine learning to decision-support tasks versus automation
- (2019) Trevor Jamieson et al. BMJ Quality & Safety
- Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence
- (2019) Shantanu Nundy et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Artificial Intelligence and the Implementation Challenge
- (2019) James Shaw et al. JOURNAL OF MEDICAL INTERNET RESEARCH
- A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock
- (2019) Heather M. Giannini et al. CRITICAL CARE MEDICINE
- Prognostic models will be victims of their own success, unless…
- (2019) Matthew C Lenert et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Do no harm: a roadmap for responsible machine learning for health care
- (2019) Jenna Wiens et al. NATURE MEDICINE
- Sepsis Rapid Response Teams
- (2018) Tammy Ju et al. CRITICAL CARE CLINICS
- An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU
- (2018) Shamim Nemati et al. CRITICAL CARE MEDICINE
- Recurrent Neural Networks for Multivariate Time Series with Missing Values
- (2018) Zhengping Che et al. Scientific Reports
- Prehospital antibiotics in the ambulance for sepsis: a multicentre, open label, randomised trial
- (2018) Nadia Alam et al. Lancet Respiratory Medicine
- Antibiotics for Sepsis—Finding the Equilibrium
- (2018) Michael Klompas et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
- (2018) Milena A. Gianfrancesco et al. JAMA Internal Medicine
- Machine learning for real-time prediction of complications in critical care: a retrospective study
- (2018) Alexander Meyer et al. Lancet Respiratory Medicine
- Ensuring Fairness in Machine Learning to Advance Health Equity
- (2018) Alvin Rajkomar et al. ANNALS OF INTERNAL MEDICINE
- High-performance medicine: the convergence of human and artificial intelligence
- (2018) Eric J. Topol NATURE MEDICINE
- Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study
- (2018) Kristin M. Corey et al. PLOS MEDICINE
- Deep Learning in Medicine—Promise, Progress, and Challenges
- (2018) Fei Wang et al. JAMA Internal Medicine
- Minimal Impact of Implemented Early Warning Score and Best Practice Alert for Patient Deterioration*
- (2018) Armando D. Bedoya et al. CRITICAL CARE MEDICINE
- Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016
- (2017) Andrew Rhodes et al. INTENSIVE CARE MEDICINE
- Unintended Consequences of Machine Learning in Medicine
- (2017) Federico Cabitza et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Managing sepsis: Electronic recognition, rapid response teams, and standardized care save lives
- (2017) Faheem W. Guirgis et al. JOURNAL OF CRITICAL CARE
- Poor performance of quick-SOFA (qSOFA) score in predicting severe sepsis and mortality – a prospective study of patients admitted with infection to the emergency department
- (2017) Åsa Askim et al. Scandinavian Journal of Trauma Resuscitation & Emergency Medicine
- Barriers to Achieving Economies of Scale in Analysis of EHR Data
- (2017) Mark Sendak et al. Applied Clinical Informatics
- Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach
- (2016) R. Andrew Taylor et al. ACADEMIC EMERGENCY MEDICINE
- A computational approach to early sepsis detection
- (2016) Jacob S. Calvert et al. COMPUTERS IN BIOLOGY AND MEDICINE
- The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
- (2016) Mervyn Singer et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- MIMIC-III, a freely accessible critical care database
- (2016) Alistair E.W. Johnson et al. Scientific Data
- A targeted real-time early warning score (TREWScore) for septic shock
- (2015) Katharine E. Henry et al. Science Translational Medicine
- Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection
- (2015) Constance D. Lehman et al. JAMA Internal Medicine
- Identifying Severe Sepsis via Electronic Surveillance
- (2014) Bristol N. Brandt et al. AMERICAN JOURNAL OF MEDICAL QUALITY
- Development, implementation, and impact of an automated early warning and response system for sepsis
- (2014) Craig A. Umscheid et al. Journal of Hospital Medicine
- Implementing the Learning Health System: From Concept to Action
- (2013) Sarah M. Greene et al. ANNALS OF INTERNAL MEDICINE
- Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research
- (2013) Ewout W. Steyerberg et al. PLOS MEDICINE
- Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science
- (2009) Laura J Damschroder et al. Implementation Science
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now