[HTML][HTML] Opening the black box: the promise and limitations of explainable machine learning in cardiology
Many clinicians remain wary of machine learning because of longstanding concerns about
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …
Emerging technologies for molecular diagnosis of sepsis
M Sinha, J Jupe, H Mack, TP Coleman… - Clinical microbiology …, 2018 - Am Soc Microbiol
Rapid and accurate profiling of infection-causing pathogens remains a significant challenge
in modern health care. Despite advances in molecular diagnostic techniques, blood culture …
in modern health care. Despite advances in molecular diagnostic techniques, blood culture …
What clinicians want: contextualizing explainable machine learning for clinical end use
S Tonekaboni, S Joshi… - Machine learning …, 2019 - proceedings.mlr.press
Translating machine learning (ML) models effectively to clinical practice requires
establishing clinicians' trust. Explainability, or the ability of an ML model to justify its …
establishing clinicians' trust. Explainability, or the ability of an ML model to justify its …
A machine learning algorithm to predict severe sepsis and septic shock: development, implementation, and impact on clinical practice
Objectives: Develop and implement a machine learning algorithm to predict severe sepsis
and septic shock and evaluate the impact on clinical practice and patient outcomes. Design …
and septic shock and evaluate the impact on clinical practice and patient outcomes. Design …
A targeted real-time early warning score (TREWScore) for septic shock
Sepsis is a leading cause of death in the United States, with mortality highest among
patients who develop septic shock. Early aggressive treatment decreases morbidity and …
patients who develop septic shock. Early aggressive treatment decreases morbidity and …
[HTML][HTML] Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study
MP Sendak, W Ratliff, D Sarro, E Alderton… - JMIR medical …, 2020 - medinform.jmir.org
Background: Successful integrations of machine learning into routine clinical care are
exceedingly rare, and barriers to its adoption are poorly characterized in the literature …
exceedingly rare, and barriers to its adoption are poorly characterized in the literature …
A computational approach to early sepsis detection
JS Calvert, DA Price, UK Chettipally, CW Barton… - Computers in biology …, 2016 - Elsevier
Objective To develop high-performance early sepsis prediction technology for the general
patient population. Methods Retrospective analysis of adult patients admitted to the …
patient population. Methods Retrospective analysis of adult patients admitted to the …
Comparison of early warning scoring systems for hospitalized patients with and without infection at risk for in-hospital mortality and transfer to the intensive care unit
VX Liu, Y Lu, KA Carey, ER Gilbert, M Afshar… - JAMA network …, 2020 - jamanetwork.com
Importance Risk scores used in early warning systems exist for general inpatients and
patients with suspected infection outside the intensive care unit (ICU), but their relative …
patients with suspected infection outside the intensive care unit (ICU), but their relative …
Delay within the 3-hour surviving sepsis campaign guideline on mortality for patients with severe sepsis and septic shock
Objectives: To specify when delays of specific 3-hour bundle Surviving Sepsis Campaign
guideline recommendations applied to severe sepsis or septic shock become harmful and …
guideline recommendations applied to severe sepsis or septic shock become harmful and …
Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data
Background Rapid antibiotic administration is known to improve sepsis outcomes, however
early diagnosis remains challenging due to complex presentation. Our objective was to …
early diagnosis remains challenging due to complex presentation. Our objective was to …