Titlebook: Explainable AI in Healthcare and Medicine; Building a Culture o Arash Shaban-Nejad,Martin Michalowski,David L. Buc Book 2021 The Editor(s)

禍害隱伏

Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Reinforcement Learning,, i.e., the percentage of normoglycemia, . for the adults and . for the adolescents, which outperforms previous approaches significantly. These results indicate that deep RL has great potential to improve the treatment of chronic diseases such as diabetes.

maverick

A Generalizable Method for Automated Quality Control of Functional Neuroimaging Datasets,performed, expert reviewers visually inspect individual raw scans and preprocessed derivatives to determine viability of the data. This Quality Control (QC) process is labor intensive, and the inability to adequately automate at large scale has proven to be a limiting factor in clinical neuroscience

哥哥噴涌而出

Critical

orthodox

Robust

Mnemonics

Natural vs. Artificially Sweet Tweets: Characterizing Discussions of Non-nutritive Sweeteners on Twmodel analysis and characterize tweet volumes over time, showing a diversity of sweetener-related content and discussion. Our findings suggest a variety of research questions that these data may support.

顛簸地移動

On-line (TweetNet) and Off-line (EpiNet): The Distinctive Structures of the Infectious,l and structural patterns. They showed the divergent sensitivities in the spike timing and retweet patterns compared to simulated RandomNet. High self-clustering patterns by governmental and public tweets can hinder efficient communication/information spreading. Epidemic related social media surveil

Constant

Medication Regimen Extraction from Medical Conversations,erformance. Compared to the baseline, our best-performing models improve the dosage and frequency extractions’ ROUGE-1 F1 scores from 54.28 and 37.13 to 89.57 and 45.94, respectively. Using our best-performing model, we present the first fully automated system that can extract Medication Regimen tag

證實

https://doi.org/10.1007/978-3-030-53352-6Health Intelligence; Precession Medicine; Precession Health; Digital Medicine; Big Data; Predictive Analy