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Machine learning and statistical models for analyzing multilevel patent data.

• Authors : Qi S; Zhejiang Provincial Center for Medical Science Technology and Education Development, Hangzhou, 310000, Zhejiang, China.; Zhang Y

Subjects: Models, Statistical* ; Machine Learning*; Humans

• Source: Scientific reports [Sci Rep] 2023 Aug 07; Vol. 13 (1), pp. 12783. Date of Electronic Publication: 2023 Aug 07.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Statistical models versus machine learning for competing risks: development and validation of prognostic models.

• Authors : Kantidakis G; Mathematical Institute (MI) Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands. .; Department of Biomedical Data Sciences, Section Medical Statistics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands. .

Subjects: Models, Statistical* ; Machine Learning*; Humans

• Source: BMC medical research methodology [BMC Med Res Methodol] 2023 Feb 24; Vol. 23 (1), pp. 51. Date of Electronic Publication: 2023 Feb 24.Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288

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Statistical approaches to identifying significant differences in predictive performance between machine learning and classical statistical models for survival data.

• Authors : Nasejje JB; School of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg, Gauteng, South Africa.; Whata A

Subjects: Models, Statistical* ; Machine Learning*; Random Forest

• Source: PloS one [PLoS One] 2022 Dec 28; Vol. 17 (12), pp. e0279435. Date of Electronic Publication: 2022 Dec 28 (Print Publication: 2022).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Sex estimation from the greater sciatic notch: a comparison of classical statistical models and machine learning algorithms.

• Authors : Knecht S; Faculté de Médecine, Université de Lorraine, 9 avenue de la Forêt de Haye, 54500, Vandœuvre-les-Nancy, France. .; Faculté de Médecine, Institut Universitaire d'Anthropologie Médico-Légale Université Côte d'Azur, 28 Avenue de Valombrose, 06107 cedex 2, Nice, France. .

Subjects: Algorithms* ; Machine Learning* ; Models, Statistical*

• Source: International journal of legal medicine [Int J Legal Med] 2021 Nov; Vol. 135 (6), pp. 2603-2613. Date of Electronic Publication: 2021 Sep 23.Publisher: Springer International Country of Publication: Germany NLM ID: 9101456 Publication Model: Print-Electronic Cited Medium: Internet ISSN:

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Machine Learning Compared With Conventional Statistical Models for Predicting Myocardial Infarction Readmission and Mortality: A Systematic Review.

• Authors : Cho SM; Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada; University of Toronto, Toronto, Ontario, Canada.

Subjects: Machine Learning* ; Models, Statistical* ; Patient Readmission*

• Source: The Canadian journal of cardiology [Can J Cardiol] 2021 Aug; Vol. 37 (8), pp. 1207-1214. Date of Electronic Publication: 2021 Mar 05.Publisher: Elsevier Country of Publication: England NLM ID: 8510280 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1916-7075

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Applied Science & Technology Full Text (H.W. Wilson)

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Evaluation of marker selection methods and statistical models for chronological age prediction based on DNA methylation.

• Authors : Lau PY; Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China.; Fung WK

Subjects: Algorithms* ; DNA Methylation* ; Machine Learning*

• Source: Legal medicine (Tokyo, Japan) [Leg Med (Tokyo)] 2020 Nov; Vol. 47, pp. 101744. Date of Electronic Publication: 2020 Jul 01.Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 100889186 Publication Model: Print-Electronic Cited Medium: Internet

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Agricola

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Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury.

• Authors : Hale AT; 1Surgical Outcomes Center for Kids, and.; 2Vanderbilt University School of Medicine, Medical Scientist Training Program.

Subjects: International Classification of Diseases*/International Classification of Diseases*/International Classification of Diseases*/standards ; International Classification of Diseases*/International Classification of Diseases*/International Classification of Diseases*/trends ; Models, Statistical*

• Source: Neurosurgical focus [Neurosurg Focus] 2018 Nov 01; Vol. 45 (5), pp. E2.Publisher: American Association of Neurological Surgeons Country of Publication: United States NLM ID: 100896471 Publication Model: Print Cited Medium:

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EXTRACTING ACTIONABLE INSIGHTS FROM TEXT DATA: A STABLE TOPIC MODEL APPROACH.

• Authors : Yang, Yi; Subramanyam, Ramanath

Subjects: STATISTICAL models; MACHINE learning; TEXT mining

• Source: MIS Quarterly. Sep2023, Vol. 47 Issue 3, p923-954. 32p. 1 Diagram, 12 Charts, 5 Graphs.

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