000			03874cam a2200565Ii 4500
001			9780429345159
003			FlBoTFG
005			20220531132314.0
006			m o d
007			cr cnu|||unuuu
008			200522t20202020flu ob 001 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9780429345159 _q(electronic bk.)
020			_a0429345151 _q(electronic bk.)
020			_z9780367363031
020			_z9780367362928
020			_a9781000767018 _q(electronic bk. : Mobipocket)
020			_a1000767019 _q(electronic bk. : Mobipocket)
020			_a9781000767308 _q(electronic bk. : EPUB)
020			_a1000767302 _q(electronic bk. : EPUB)
020			_a9781000766721 _q(electronic bk. : PDF)
020			_a1000766721 _q(electronic bk. : PDF)
035			_a(OCoLC)1155202718 _z(OCoLC)1155638023
035			_a(OCoLC-P)1155202718
050		4	_aR859.7.A78 _bC3 2020
072		7	_aBUS _x061000 _2bisacsh
072		7	_aCOM _x037000 _2bisacsh
072		7	_aMAT _x029000 _2bisacsh
072		7	_aPBT _2bicssc
082	0	4	_a610.28563 _223
100	1		_aChang, Mark, _eauthor.
245	1	0	_aArtificial intelligence for drug development, precision medicine, and healthcare / _cMark Chang.
264		1	_aBoca Raton, FL : _bCRC Press, _c[2020]
264		4	_c©2020
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aChapman & Hall/CRC biostatistics series
500			_a"A Chapman & Hall book".
505	0		_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.
520			_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.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aArtificial intelligence _xMedical applications.
650		7	_aBUSINESS & ECONOMICS / Statistics _2bisacsh
650		7	_aCOMPUTERS / Machine Theory _2bisacsh
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780429345159
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c70541 _d70541