LEADER 03358oam 2200481I 450 001 9910807439303321 005 20230822234355.0 010 $a1-000-76672-1 010 $a0-429-34515-1 024 7 $a10.1201/9780429345159 035 $a(CKB)4100000011241796 035 $a(MiAaPQ)EBC6192129 035 $a(OCoLC)1155202718$z(OCoLC)1155638023 035 $a(OCoLC-P)1155202718 035 $a(FlBoTFG)9780429345159 035 $a(EXLCZ)994100000011241796 100 $a20200522h20202020 uy 0 101 0 $aeng 135 $aurcnu|||unuuu 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial intelligence for drug development, precision medicine, and healthcare /$fMark Chang 210 1$aBoca Raton, FL :$cCRC Press,$d[2020] 210 4$dİ2020 215 $a1 online resource (xv, 355 pages) $cillustrations 225 1 $aChapman & Hall/CRC biostatistics series 300 $a"A Chapman & Hall book". 311 $a0-367-36292-9 320 $aIncludes bibliographical references and index. 327 $a1. Overview of Modern Artificial Intelligence. 2. Classic Statistics and Modern Machine Learning. 3. Similarity Principle- Fundamental Principle of All Sciences. 4. Similarity-Principle-Based Artificial Intelligence. 5. Artificial Neural Network. 6. Deep Learning Neural Network. 7. Kernel Methods. 8. Decision Tree and Ensemble Methods. 9. Bayesian Learning Approach. 10. Unsupervised Learning. 11. Reinforcement Learning. 12.Swarm and Evolutionary Intelligence. 13. Applications of AI in Medical Science and Drug Development. 14. Future Perspectives-Artificial General Intelligence. 330 $aArtificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer sciences use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book: Covers broad AI topics in drug development, precision medicine, and healthcare. Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods. Introduces the similarity principle and related AI methods for both big and small data problems. Offers a balance of statistical and algorithm-based approaches to AI. Provides examples and real-world applications with hands-on R code. Suggests the path forward for AI in medicine and artificial general intelligence. As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code. 410 0$aChapman & Hall/CRC biostatistics series. 606 $aArtificial intelligence$xMedical applications 615 0$aArtificial intelligence$xMedical applications. 676 $a610.28563 700 $aChang$b Mark$0520702 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910807439303321 996 $aArtificial intelligence for drug development, precision medicine, and healthcare$94120702 997 $aUNINA