Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare

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A01=Mark Chang
algorithm
algorithm-based approaches
Artificial General Intelligence
artificial intelligence
Author_Mark Chang
biomedical data analysis
Breast Cancer Decision Tree
CA
Category=PBT
clinical trials
computational pharmacology
CPTs
CSV File
Deep Belief Networks
Distance Inverse Function
drug development
Drug Safety Monitoring
Elastic Net Regularization
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
evolutionary algorithms in medicine
GA
Gaussian RBF Kernel
Global Clustering Coefficient
Grey Wolf Optimizer
healthcare work system
machine learning
MDP.
Minimum Systolic Blood Pressure
Ml Algorithm
Multiple Layer Perceptron
neural network optimization
pharmaceutical science
POMDP
precision medicine
PSO
R programming applications
Random Forest
reinforcement learning models
statistical learning methods
SVM
Swarm Intelligence
Testset
Unsupervised Learning

Product details

  • ISBN 9780367362928
  • Weight: 861g
  • Dimensions: 156 x 234mm
  • Publication Date: 05 May 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer science’s use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book:

· Covers broad AI topics in drug development, precision medicine, and healthcare.

· Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods.

· Introduces the similarity principle and related AI methods for both big and small data problems.

· Offers a balance of statistical and algorithm-based approaches to AI.

· Provides examples and real-world applications with hands-on R code.

· Suggests the path forward for AI in medicine and artificial general intelligence.

As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code.

Mark Chang is the founder of AGInception. With 12 published books, he is an adjunct professor at Boston University, an elected Fellow of the American Statistical Association, and a cofounder of the International Society for Biopharmaceutical Statistics.

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