Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: May 4, 2018
Open Peer Review Period: May 9, 2018 - Jul 4, 2018
Date Accepted: Jan 27, 2021
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning”

Chiavegatto Filho A, Batista AFDM, dos Santos HG

Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning”

J Med Internet Res 2021;23(2):e10969

DOI: 10.2196/10969

PMID: 33570496

PMCID: 7880048

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning”

  • Alexandre Chiavegatto Filho; 
  • André Filipe De Moraes Batista; 
  • Hellen Geremias dos Santos

 Citation

Please cite as:

Chiavegatto Filho A, Batista AFDM, dos Santos HG

Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning”

J Med Internet Res 2021;23(2):e10969

DOI: 10.2196/10969

PMID: 33570496

PMCID: 7880048

Per the author's request the PDF is not available.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.