Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare
Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …
derive insights from clinical data and improve patient outcomes. However, these highly …
Mining electronic health records (EHRs) A survey
The continuously increasing cost of the US healthcare system has received significant
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
Big data in health care: using analytics to identify and manage high-risk and high-cost patients
The US health care system is rapidly adopting electronic health records, which will
dramatically increase the quantity of clinical data that are available electronically …
dramatically increase the quantity of clinical data that are available electronically …
[HTML][HTML] Predicting healthcare trajectories from medical records: A deep learning approach
Personalized predictive medicine necessitates the modeling of patient illness and care
processes, which inherently have long-term temporal dependencies. Healthcare …
processes, which inherently have long-term temporal dependencies. Healthcare …
Word cloud explorer: Text analytics based on word clouds
F Heimerl, S Lohmann, S Lange… - 2014 47th Hawaii …, 2014 - ieeexplore.ieee.org
Word clouds have emerged as a straightforward and visually appealing visualization
method for text. They are used in various contexts as a means to provide an overview by …
method for text. They are used in various contexts as a means to provide an overview by …
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 …
Deepcare: A deep dynamic memory model for predictive medicine
Personalized predictive medicine necessitates modeling of patient illness and care
processes, which inherently have long-term temporal dependencies. Healthcare …
processes, which inherently have long-term temporal dependencies. Healthcare …
[HTML][HTML] The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit
Background Severe sepsis and septic shock are still the leading causes of death in Intensive
Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of …
Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of …
Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research
Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern
medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the …
medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the …
[HTML][HTML] Clinical applications of artificial intelligence in sepsis: a narrative review
M Schinkel, K Paranjape, RSN Panday… - Computers in biology …, 2019 - Elsevier
Many studies have been published on a variety of clinical applications of artificial
intelligence (AI) for sepsis, while there is no overview of the literature. The aim of this review …
intelligence (AI) for sepsis, while there is no overview of the literature. The aim of this review …