Literature DB >> 28327989

Using and understanding cross-validation strategies. Perspectives on Saeb et al.

Max A Little1, Gael Varoquaux2, Sohrab Saeb3, Luca Lonini4, Arun Jayaraman4, David C Mohr3, Konrad P Kording3,4.   

Abstract

This three-part review takes a detailed look at the complexities of cross-validation, fostered by the peer review of Saeb et al.'s paper entitled "The need to approximate the use-case in clinical machine learning." It contains perspectives by reviewers and by the original authors that touch upon cross-validation: the suitability of different strategies and their interpretation.
© The Author 2017. Published by Oxford University Press.

Entities:  

Keywords:  clinical applications; cross-validation; machine learning

Mesh：

Year:  2017        PMID： 28327989      PMCID： PMC5441396          DOI： 10.1093/gigascience/gix020

Source DB:  PubMed          Journal:  Gigascience        ISSN： 2047-217X            Impact factor:   6.524


  7 in total

1.  Activity recognition in patients with lower limb impairments: do we need training data from each patient?

Authors:  Luca Lonini; Aakash Gupta; Konrad Kording; Arun Jayaraman
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2016-08

Review 2.  Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines.

Authors:  Gaël Varoquaux; Pradeep Reddy Raamana; Denis A Engemann; Andrés Hoyos-Idrobo; Yannick Schwartz; Bertrand Thirion
Journal:  Neuroimage       Date:  2016-10-29       Impact factor: 6.556

3.  Predicting brain-age from multimodal imaging data captures cognitive impairment.

Authors:  Franziskus Liem; Gaël Varoquaux; Jana Kynast; Frauke Beyer; Shahrzad Kharabian Masouleh; Julia M Huntenburg; Leonie Lampe; Mehdi Rahim; Alexandre Abraham; R Cameron Craddock; Steffi Riedel-Heller; Tobias Luck; Markus Loeffler; Matthias L Schroeter; Anja Veronica Witte; Arno Villringer; Daniel S Margulies
Journal:  Neuroimage       Date:  2016-11-23       Impact factor: 6.556

4.  The need to approximate the use-case in clinical machine learning.

Authors:  Sohrab Saeb; Luca Lonini; Arun Jayaraman; David C Mohr; Konrad P Kording
Journal:  Gigascience       Date:  2017-05-01       Impact factor: 6.524

5.  Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests.

Authors:  Athanasios Tsanas; Max A Little; Patrick E McSharry; Lorraine O Ramig
Journal:  IEEE Trans Biomed Eng       Date:  2009-11-20       Impact factor: 4.538

6.  Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example.

Authors:  Alexandre Abraham; Michael P Milham; Adriana Di Martino; R Cameron Craddock; Dimitris Samaras; Bertrand Thirion; Gael Varoquaux
Journal:  Neuroimage       Date:  2016-11-16       Impact factor: 7.400

7.  Making Activity Recognition Robust against Deceptive Behavior.

Authors:  Sohrab Saeb; Konrad Körding; David C Mohr
Journal:  PLoS One       Date:  2015-12-11       Impact factor: 3.240
  7 in total
  23 in total

1.  Depression Screening from Voice Samples of Patients Affected by Parkinson's Disease.

Authors:  Yasin Ozkanca; Miraç Göksu Öztürk; Merve Nur Ekmekci; David C Atkins; Cenk Demiroglu; Reza Hosseini Ghomi
Journal:  Digit Biomark       Date:  2019-06-12

2.  The need to approximate the use-case in clinical machine learning.

Authors:  Sohrab Saeb; Luca Lonini; Arun Jayaraman; David C Mohr; Konrad P Kording
Journal:  Gigascience       Date:  2017-05-01       Impact factor: 6.524

Review 3.  A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining.

Authors:  Mahsa Mansourian; Sadaf Khademi; Hamid Reza Marateb
Journal:  Diagnostics (Basel)       Date:  2021-02-25

4.  Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry.

Authors:  Minh Doan; Claire Barnes; Claire McQuin; Juan C Caicedo; Allen Goodman; Anne E Carpenter; Paul Rees
Journal:  Nat Protoc       Date:  2021-06-18       Impact factor: 13.491

5.  How to remove or control confounds in predictive models, with applications to brain biomarkers.

Authors:  Darya Chyzhyk; Gaël Varoquaux; Michael Milham; Bertrand Thirion
Journal:  Gigascience       Date:  2022-03-12       Impact factor: 6.524

Review 6.  Preventing dataset shift from breaking machine-learning biomarkers.

Authors:  Jérôme Dockès; Gaël Varoquaux; Jean-Baptiste Poline
Journal:  Gigascience       Date:  2021-09-28       Impact factor: 6.524

7.  Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate.

Authors:  Subash Padmanaban; Justin Baker; Bradley Greger
Journal:  Front Neurosci       Date:  2018-02-06       Impact factor: 4.677

8.  Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD.

Authors:  Siddharth Arora; Fahd Baig; Christine Lo; Thomas R Barber; Michael A Lawton; Andong Zhan; Michal Rolinski; Claudio Ruffmann; Johannes C Klein; Jane Rumbold; Amandine Louvel; Zenobia Zaiwalla; Graham Lennox; Tim Quinnell; Gary Dennis; Richard Wade-Martins; Yoav Ben-Shlomo; Max A Little; Michele T Hu
Journal:  Neurology       Date:  2018-09-19       Impact factor: 11.800

9.  Remote smartphone monitoring of Parkinson's disease and individual response to therapy.

Authors:  Larsson Omberg; Elias Chaibub Neto; Thanneer M Perumal; Abhishek Pratap; Aryton Tediarjo; Jamie Adams; Bastiaan R Bloem; Brian M Bot; Molly Elson; Samuel M Goldman; Michael R Kellen; Karl Kieburtz; Arno Klein; Max A Little; Ruth Schneider; Christine Suver; Christopher Tarolli; Caroline M Tanner; Andrew D Trister; John Wilbanks; E Ray Dorsey; Lara M Mangravite
Journal:  Nat Biotechnol       Date:  2021-08-09       Impact factor: 54.908

10.  Machine learning models predicting multidrug resistant urinary tract infections using "DsaaS".

Authors:  Alessio Mancini; Leonardo Vito; Elisa Marcelli; Marco Piangerelli; Renato De Leone; Sandra Pucciarelli; Emanuela Merelli
Journal:  BMC Bioinformatics       Date:  2020-08-21       Impact factor: 3.169
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