Artificial Intelligence and Pharmacometrics: Time to Embrace, Capitalize, and Advance?

被引：29

作者：

Chaturvedula, Ayyappa ^{[1
]}

Calad-Thomson, Stacie ^{[2
]}

Liu, Chao ^{[3
]}

Sale, Mark ^{[4
]}

Gattu, Nandu ^{[5
]}

Goyal, Navin ^{[6
]}

机构：

[1] UNTHSC, UNT Univ North Texas Syst, Coll Pharm, Ft Worth, TX 76107 USA

[2] GlaxoSmithKline, ATOM Accelerating Therapeut Opportun Med Consorti, San Francisco, CA USA

[3] US FDA, Off Clin Pharmacol, Silver Spring, MD USA

[4] Nuventra, Raleigh, NC USA

[5] Excelra, Hyderabad, India

[6] GlaxoSmithKline, Clin Pharmacol, Collegeville, PA USA

来源：

CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY | 2019年 / 8卷 / 07期

关键词：

D O I：

10.1002/psp4.12418

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Artificial intelligence (AI) has been described as the machine for the fourth industrial revolution. Without exception, AI is predicted to change the face of every industry. In the field of drug development, AI is employed to enhance efficiency. Pharmacometrics and systems pharmacology play a vital role in the drug-development decision process. Thus, it is important to recognize and embrace the efficiencies that AI can bring to the pharmacometrics community.

引用

页码：440 / 443

页数：4

共 8 条

[1] Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer [J].

Bejnordi, Babak Ehteshami ;

Veta, Mitko ;

van Diest, Paul Johannes ;

van Ginneken, Bram ;

Karssemeijer, Nico ;

Litjens, Geert ;

van der Laak, Jeroen A. W. M. .

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2017, 318 (22) :2199-2210

[2] A genetic algorithm-based, hybrid machine learning approach to model selection [J].

Bies, RR ;

Muldoon, MF ;

Pollock, BG ;

Manuck, S ;

Smith, G ;

Sale, ME .

JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS, 2006, 33 (02) :195-221

[3] Probabilistic machine learning and artificial intelligence [J].

Ghahramani, Zoubin .

NATURE, 2015, 521 (7553) :452-459

[4]

McCarthy J., 1955, PROPOSAL DARTMOUTH S

[5] Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists [J].

Rajpurkar, Pranav ;

Irvin, Jeremy ;

Ball, Robyn L. ;

Zhu, Kaylie ;

Yang, Brandon ;

Mehta, Hershel ;

Duan, Tony ;

Ding, Daisy ;

Bagul, Aarti ;

Langlotz, Curtis P. ;

Patel, Bhavik N. ;

Yeom, Kristen W. ;

Shpanskaya, Katie ;

Blankenberg, Francis G. ;

Seekins, Jayne ;

Amrhein, Timothy J. ;

Mong, David A. ;

Halabi, Safwan S. ;

Zucker, Evan J. ;

Ng, Andrew Y. ;

Lungren, Matthew P. .

PLOS MEDICINE, 2018, 15 (11)

[6] Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy [J].

Sayres, Rory ;

Taly, Ankur ;

Rahimy, Ehsan ;

Blumer, Katy ;

Coz, David ;

Hammel, Naama ;

Krause, Jonathan ;

Narayanaswamy, Arunachalam ;

Rastegar, Zahra ;

Wu, Derek ;

Xu, Shawn ;

Barb, Scott ;

Joseph, Anthony ;

Shumski, Michael ;

Smith, Jesse ;

Sood, Arjun B. ;

Corrado, Greg S. ;

Peng, Lily ;

Webster, Dale R. .

OPHTHALMOLOGY, 2019, 126 (04) :552-564

[7] Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building [J].

Sherer, Eric A. ;

Sale, Mark E. ;

Pollock, Bruce G. ;

Belani, Chandra P. ;

Egorin, Merrill J. ;

Ivy, Percy S. ;

Lieberman, Jeffrey A. ;

Manuck, Stephen B. ;

Marder, Stephen R. ;

Muldoon, Matthew F. ;

Scher, Howard I. ;

Solit, David B. ;

Bies, Robert R. .

JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS, 2012, 39 (04) :393-414

[8] High-performance medicine: the convergence of human and artificial intelligence [J].

Topol, Eric J. .

NATURE MEDICINE, 2019, 25 (01) :44-56

← 1 →