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

Inelasticity

Renée K. Margolis,Richard U. Margolisromising concept that is gaining attention over traditional EMR-based medical information management systems. The success of intelligent solutions in health and medicine depends on the degree to which they support interoperability, to allow consistent integration of different systems and data source

IST

The Synthesis of Acetylcholine,milarity measures, how to accurately and efficiently retrieve similar patients from large scale healthcare data remains less explored. Similar patient retrieval has become increasingly important and challenging as the volume of healthcare data grows rapidly. To address the challenge, we propose a co

六邊形

典型

Compartmentation of Amino Acid Metabolism,me wearables, limiting usability. This study applies machine learning to discriminate six stages of PD. The data was acquired by low cost walker-mounted sensors at a movement disorders clinic. A large set of features were extracted and three feature selection methods were compared using a Random For

表狀態(tài)

Neurochemistry of Parkinsonism,dilated recurrent neural networks are used to learn the control strategy, trained by a variant of Q-learning. The inputs to the model include the real-time sensed glucose and meal carbohydrate content, and the outputs are the actions necessary to deliver dual-hormone (basal insulin and glucagon) con

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DOTE

貪心

Introduction to Evolutionary Computationo foster precision medicine, e.g. by finding new disease phenotypes or performing individual disease prediction. However, to take full advantage of deep learning methods on clinical data, architectures are necessary that (1) are robust with respect to missing and wrong values, and (2) can deal with

飾帶

https://doi.org/10.1007/978-1-4899-0682-3ility data. We collect RR interval time series data from nearly 100 firefighter trainees that participated in a drill. We explore and compare three methods in order to perform unsupervised stress detection: (1) traditional K-Means clustering with engineered time and frequency domain features (2) con

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