Explainable artificial intelligence model to predict acute critical illness from electronic health records
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Explainable artificial intelligence model to predict acute critical illness from electronic health records
Authors
Keywords
-
Journal
Nature Communications
Volume 11, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-07-31
DOI
10.1038/s41467-020-17431-x
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Early detection of sepsis utilizing deep learning on electronic health record event sequences
- (2020) Simon Meyer Lauritsen et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning
- (2019) Benjamin Shickel et al. Scientific Reports
- An attention based deep learning model of clinical events in the intensive care unit
- (2019) Deepak A. Kaji et al. PLoS One
- Machine Learning in Medicine
- (2019) Alvin Rajkomar et al. NEW ENGLAND JOURNAL OF MEDICINE
- Causability and explainabilty of artificial intelligence in medicine
- (2019) Andreas Holzinger et al. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
- Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
- (2019) Christopher Barton et al. COMPUTERS IN BIOLOGY AND MEDICINE
- A clinically applicable approach to continuous prediction of future acute kidney injury
- (2019) Nenad Tomašev et al. NATURE
- Methods for interpreting and understanding deep neural networks
- (2018) Grégoire Montavon et al. DIGITAL SIGNAL PROCESSING
- Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
- (2018) Cao Xiao et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU
- (2018) Qingqing Mao et al. BMJ Open
- High-performance medicine: the convergence of human and artificial intelligence
- (2018) Eric J. Topol NATURE MEDICINE
- Opening the black box of machine learning
- (2018) The Lancet Respiratory Medicine Lancet Respiratory Medicine
- Prediction of sepsis patients using machine learning approach: A meta-analysis
- (2018) Md. Mohaimenul Islam et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Unintended Consequences of Machine Learning in Medicine
- (2017) Federico Cabitza et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Explaining nonlinear classification decisions with deep Taylor decomposition
- (2017) Grégoire Montavon et al. PATTERN RECOGNITION
- Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
- (2017) Benjamin Shickel et al. IEEE Journal of Biomedical and Health Informatics
- A computational approach to early sepsis detection
- (2016) Jacob S. Calvert et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Assessment of Clinical Criteria for Sepsis
- (2016) Christopher W. Seymour et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
- (2016) Mervyn Singer et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Deep learning
- (2015) Yann LeCun et al. NATURE
- On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
- (2015) Sebastian Bach et al. PLoS One
- Explaining prediction models and individual predictions with feature contributions
- (2013) Erik Štrumbelj et al. KNOWLEDGE AND INFORMATION SYSTEMS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now