Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations
Artificial intelligence has been advancing in fields including anesthesiology. This scoping
review of the intersection of artificial intelligence and anesthesia research identified and …
review of the intersection of artificial intelligence and anesthesia research identified and …
Artificial intelligence and machine learning in anesthesiology
CW Connor - Anesthesiology, 2019 - pubs.asahq.org
Commercial applications of artificial intelligence and machine learning have made
remarkable progress recently, particularly in areas such as image recognition, natural …
remarkable progress recently, particularly in areas such as image recognition, natural …
Emergency department triage prediction of clinical outcomes using machine learning models
Background Development of emergency department (ED) triage systems that accurately
differentiate and prioritize critically ill from stable patients remains challenging. We used …
differentiate and prioritize critically ill from stable patients remains challenging. We used …
[HTML][HTML] Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury
Objective We aimed to explore the added value of common machine learning (ML)
algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study …
algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study …
Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
C Barton, U Chettipally, Y Zhou, Z Jiang… - Computers in biology …, 2019 - Elsevier
Objective Sepsis remains a costly and prevalent syndrome in hospitals; however, machine
learning systems can increase timely sepsis detection using electronic health records. This …
learning systems can increase timely sepsis detection using electronic health records. This …
Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory
Background Unplanned readmission of a hospitalized patient is an indicator of patients'
exposure to risk and an avoidable waste of medical resources. In addition to hospital …
exposure to risk and an avoidable waste of medical resources. In addition to hospital …
Clinician involvement in research on machine learning–based predictive clinical decision support for the hospital setting: A scoping review
JM Schwartz, AJ Moy, SC Rossetti… - Journal of the …, 2021 - academic.oup.com
Objective The study sought to describe the prevalence and nature of clinical expert
involvement in the development, evaluation, and implementation of clinical decision support …
involvement in the development, evaluation, and implementation of clinical decision support …
Optimal intensive care outcome prediction over time using machine learning
Background Prognostication is an essential tool for risk adjustment and decision making in
the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to …
the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to …
Application of machine learning to predict the outcome of pediatric traumatic brain injury
T Thara, O Thakul - Chinese Journal of Traumatology, 2021 - mednexus.org
Purpose: Traumatic brain injury (TBI) generally causes mortality and disability, particularly in
children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool …
children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool …
Causal imitation learning under temporally correlated noise
We develop algorithms for imitation learning from policy data that was corrupted by
temporally correlated noise in expert actions. When noise affects multiple timesteps of …
temporally correlated noise in expert actions. When noise affects multiple timesteps of …