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Literature DB >> 30100822

Statistics versus machine learning.

Danilo Bzdok¹, Naomi Altman², Martin Krzywinski³.

Abstract

Entities: Disease Species

Mesh：

Year: 2018 PMID： 30100822 PMCID： PMC6082636 DOI： 10.1038/nmeth.4642

Source DB: PubMed Journal: Nat Methods ISSN： 1548-7091 Impact factor: 28.547

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3 in total

1. Sampling distributions and the bootstrap.

Authors: Anthony Kulesa; Martin Krzywinski; Paul Blainey; Naomi Altman
Journal: Nat Methods Date: 2015-06 Impact factor: 28.547

2. Points of Significance: Machine learning: a primer.

Authors: Danilo Bzdok; Martin Krzywinski; Naomi Altman
Journal: Nat Methods Date: 2017-11-30 Impact factor: 28.547

Review 3. Classical Statistics and Statistical Learning in Imaging Neuroscience.

Authors: Danilo Bzdok
Journal: Front Neurosci Date: 2017-10-06 Impact factor: 4.677

3 in total

133 in total

1. Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges.

Authors: David Thesmar; David Sraer; Lisa Pinheiro; Nick Dadson; Razvan Veliche; Paul Greenberg
Journal: Pharmacoeconomics Date: 2019-06 Impact factor: 4.981

2. Reply to Biskup et al. and Tu et al.: Sex differences in metabolic brain aging.

Authors: Manu S Goyal; Andrei G Vlassenko; Marcus E Raichle
Journal: Proc Natl Acad Sci U S A Date: 2019-05-28 Impact factor: 11.205

3. Deep learning and computer vision will transform entomology.

Authors: Toke T Høye; Johanna Ärje; Kim Bjerge; Oskar L P Hansen; Alexandros Iosifidis; Florian Leese; Hjalte M R Mann; Kristian Meissner; Claus Melvad; Jenni Raitoharju
Journal: Proc Natl Acad Sci U S A Date: 2021-01-12 Impact factor: 11.205

Review 4. Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department.

Authors: Kei Ouchi; Charlotta Lindvall; Peter R Chai; Edward W Boyer
Journal: J Med Toxicol Date: 2018-06-01

Statistics versus machine learning.

1. Sampling distributions and the bootstrap.

2. Points of Significance: Machine learning: a primer.

Review 3. Classical Statistics and Statistical Learning in Imaging Neuroscience.

1. Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges.

2. Reply to Biskup et al. and Tu et al.: Sex differences in metabolic brain aging.

3. Deep learning and computer vision will transform entomology.

Review 4. Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department.

Review 5. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview.

6. Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease.

Review 7. Quantitative assessment of the activity of antituberculosis drugs and regimens.

Review 8. Machine learning approaches to study glioblastoma: A review of the last decade of applications.

Review 9. Primer on machine learning: utilization of large data set analyses to individualize pain management.

Review 10. Machine learning for predicting cardiac events: what does the future hold?