Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery-Reference-Cited by-同舟云学术

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Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery

Published:2021-10 Issue:10 Volume:21 Page:1610-1616
ISSN:1529-9430
Container-title:The Spine Journal
language:en
Short-container-title:The Spine Journal

Author:

Azad Tej D.,Ehresman Jeff,Ahmed Ali Karim,Staartjes Victor E.,Lubelski Daniel,Stienen Martin N.,Veeravagu Anand,Ratliff John K.

Publisher

Elsevier BV

Subject

Neurology (clinical),Surgery,Orthopedics and Sports Medicine

Reference49 articles.

1. 1,500 scientists lift the lid on reproducibility;Baker;Nature,2016

2. Drug development: raise standards for preclinical cancer research;Begley;Nature,2012

3. Medicine contends with how to use artificial intelligence;Couzin-Frankel;Science,2019

4. Challenges to the reproducibility of machine learning models in health care;Beam;JAMA,2020

5. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness;Vollmer;BMJ,2020

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1. Applications of Artificial Intelligence and Machine Learning in Spine MRI;Bioengineering;2024-09-05

2. External Validation of Predictive Models for Failed Medical Management of Spinal Epidural Abscess;World Neurosurgery;2024-07

3. Personalizing adult spinal deformity surgery through multimodal artificial intelligence;Acta Orthopaedica et Traumatologica Turcica;2024-05-03

4. Reproducible machine learning research in mental workload classification using EEG;Frontiers in Neuroergonomics;2024-04-10

5. Simulated outcomes for durotomy repair in minimally invasive spine surgery;Scientific Data;2024-01-10

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