Use of artificial intelligence in improving outcomes in heart disease: a scientific statement from the American Heart Association
A major focus of academia, industry, and global governmental agencies is to develop and
apply artificial intelligence and other advanced analytical tools to transform health care …
apply artificial intelligence and other advanced analytical tools to transform health care …
Model‐informed reinforcement learning for enabling precision dosing via adaptive dosing
Precision dosing, the tailoring of drug doses to optimize therapeutic benefits and minimize
risks in each patient, is essential for drugs with a narrow therapeutic window and severe …
risks in each patient, is essential for drugs with a narrow therapeutic window and severe …
A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis
XD Wu, RC Li, Z He, TZ Yu, CQ Cheng - NPJ Digital Medicine, 2023 - nature.com
Abstract Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting
clinicians for real-time treatment of sepsis. While a value function quantifies the performance …
clinicians for real-time treatment of sepsis. While a value function quantifies the performance …
[HTML][HTML] Artificial intelligence in perioperative medicine: a narrative review
Recent advancements in artificial intelligence (AI) techniques have enabled the
development of accurate prediction models using clinical big data. AI models for …
development of accurate prediction models using clinical big data. AI models for …
[HTML][HTML] Effective data-driven precision medicine by cluster-applied deep reinforcement learning
The significance of machine-learning approaches in the healthcare domain has grown
rapidly owing to the existence of enormous amounts of data and well-established simulation …
rapidly owing to the existence of enormous amounts of data and well-established simulation …
[HTML][HTML] Applications of artificial intelligence in anesthesia: a systematic review
This review article examines the utility of artificial intelligence (AI) in anesthesia, with a focus
on recent developments and future directions in the field. A total of 19,300 articles were …
on recent developments and future directions in the field. A total of 19,300 articles were …
Development and validation of a reinforcement learning model for ventilation control during emergence from general anesthesia
Ventilation should be assisted without asynchrony or cardiorespiratory instability during
anesthesia emergence until sufficient spontaneous ventilation is recovered. In this …
anesthesia emergence until sufficient spontaneous ventilation is recovered. In this …
Deep reinforcement learning-based propofol infusion control for anesthesia: A feasibility study with a 3000-subject dataset
In this work, we present a deep reinforcement learning-based approach as a baseline
system for autonomous propofol infusion control. Specifically, design an environment for …
system for autonomous propofol infusion control. Specifically, design an environment for …
Value function assessment to different RL algorithms for heparin treatment policy of patients with sepsis in ICU
J Liu, Y **e, X Shu, Y Chen, Y Sun, K Zhong… - Artificial Intelligence in …, 2024 - Elsevier
Heparin is a critical aspect of managing sepsis after abdominal surgery, which can improve
microcirculation, protect organ function, and reduce mortality. However, there is no clinical …
microcirculation, protect organ function, and reduce mortality. However, there is no clinical …
Development of a Bispectral index score prediction model based on an interpretable deep learning algorithm
Background Proper maintenance of hypnosis is crucial for ensuring the safety of patients
undergoing surgery. Accordingly, indicators, such as the Bispectral index (BIS), have been …
undergoing surgery. Accordingly, indicators, such as the Bispectral index (BIS), have been …