Machine Learning and Statistics in Clinical Research Articles—Moving Past the False Dichotomy-Reference-Cited by-同舟云学术

Machine Learning and Statistics in Clinical Research Articles—Moving Past the False Dichotomy

Published:2023-05-01 Issue:5 Volume:177 Page:448
ISSN:2168-6203
Container-title:JAMA Pediatrics
language:en
Short-container-title:JAMA Pediatr

Author:

Finlayson Samuel G.¹²,Beam Andrew L.³,van Smeden Maarten⁴

Affiliation:

1. Department of Pediatrics, Seattle Children’s Hospital, Seattle, Washington

2. Department of Genetics, University of Washington, Seattle

3. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

4. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands

Abstract

This Viewpoint describes the false dichotomy between statistics and machine learning and suggests considerations in building and evaluating clinical prediction models.

Publisher

American Medical Association (AMA)

Subject

Pediatrics, Perinatology and Child Health

Link

https://jamanetwork.com/journals/jamapediatrics/articlepdf/2802298/jamapediatrics_finlayson_2023_vp_230002_1682695658.28904.pdf

Reference9 articles.

1. [Review of] Bloomfield B ed. The question of artificial intelligence.;McCarthy;Ann Hist Comput,1988

2. Some studies in machine learning using the game of checkers.;Samuel;IBM J Res Develop,1959

3. Statistical modeling: the two cultures (with comments and a rejoinder by the author).;Breiman;SSO Schweiz Monatsschr Zahnheilkd,2001

4. Developing, validating, updating and judging the impact of prognostic models for respiratory diseases.;van Royen;Eur Respir J,2022

5. Calibration: the Achilles heel of predictive analytics.;Van Calster;BMC Med,2019

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study;The Lancet Digital Health;2024-08

2. Statistical analysis of observational studies in disability research;Developmental Medicine & Child Neurology;2024-05-09

3. Artificial intelligence in surgery;Nature Medicine;2024-05

4. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods;BMJ;2024-04-16

5. Application of Machine Learning and Deep EfficientNets in Distinguishing Neonatal Adrenal Hematomas From Neuroblastoma in Enhanced Computed Tomography Images;World Journal of Oncology;2024-02