Authors’ Response to Peer Reviews of “Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis”

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Related collections

Most cited references 11

Record: found
Abstract: found
Article: not found

Can machine learning improve mortality prediction following cardiac surgery?

Umberto Benedetto, Shubhra Sinha, Matt Lyon … (2020)

Interest in the clinical usefulness of machine learning for risk prediction has bloomed recently. Cardiac surgery patients are at high risk of complications and therefore presurgical risk assessment is of crucial relevance. We aimed to compare the performance of machine learning algorithms over traditional logistic regression (LR) model to predict in-hospital mortality following cardiac surgery.

0 comments Cited 14 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft: Improving Risk Prediction With Intraoperative Events Using Gradient Boosting.

Makoto Mori, Thomas J S Durant, Chenxi Huang … (2021)

Intraoperative data may improve models predicting postoperative events. We evaluated the effect of incorporating intraoperative variables to the existing preoperative model on the predictive performance of the model for coronary artery bypass graft.

0 comments Cited 9 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

External model validation of binary clinical risk prediction models in cardiovascular and thoracic surgery

Graeme L. Hickey, Eugene H. Blackstone (2016)

0 comments Cited 7 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Tim Dong:

ORCID: http://orcid.org/0000-0003-1953-0063

Shubhra Sinha:

ORCID: http://orcid.org/0000-0001-8554-6704

Ben Zhai:

ORCID: http://orcid.org/0000-0003-1635-1406

Daniel Fudulu:

ORCID: http://orcid.org/0000-0001-6759-7719

Jeremy Chan:

ORCID: http://orcid.org/0000-0002-3009-5712

Pradeep Narayan:

ORCID: http://orcid.org/0000-0002-3843-1338

Andy Judge:

ORCID: http://orcid.org/0000-0003-3015-0432

Massimo Caputo:

ORCID: http://orcid.org/0000-0001-7508-0891

Arnaldo Dimagli:

ORCID: http://orcid.org/0000-0003-0055-8656

Umberto Benedetto:

ORCID: http://orcid.org/0000-0002-7074-7949

Gianni D Angelini:

ORCID: http://orcid.org/0000-0002-1753-3730

Journal

Journal ID (nlm-ta): JMIRx Med

Journal ID (iso-abbrev): JMIRx Med

Journal ID (hwp): JMIRxMed

Journal ID (publisher-id): xmed

Journal ID (index): 34

Title: JMIRx Med

Publisher: JMIRx Med

ISSN (Electronic): 2563-6316

Publication date Collection: 2024

Publication date (Electronic): 12 June 2024

Volume: 5

Electronic Location Identifier: e60384

Affiliations

[1 ]departmentBristol Heart Institute , Translational Health Sciences, University of Bristol , Bristol, United Kingdom

[2 ]departmentSchool of Computing Science , Northumbria University , Newcastle upon Tyne, United Kingdom

[3 ]departmentDepartment of Cardiac Surgery , Rabindranath Tagore International Institute of Cardiac Sciences , West Bengal, India

Author notes

TimDongMSc, Bristol Heart Institute, Translational Health Sciences, University of Bristol, Terrell St, Bristol, BS2 8ED, United Kingdom, 44 75 6416 8791; qd18830@ 123456bristol.ac.uk

Author information

Tim Dong http://orcid.org/0000-0003-1953-0063

Shubhra Sinha http://orcid.org/0000-0001-8554-6704

Ben Zhai http://orcid.org/0000-0003-1635-1406

Daniel Fudulu http://orcid.org/0000-0001-6759-7719

Jeremy Chan http://orcid.org/0000-0002-3009-5712

Pradeep Narayan http://orcid.org/0000-0002-3843-1338

Andy Judge http://orcid.org/0000-0003-3015-0432

Massimo Caputo http://orcid.org/0000-0001-7508-0891

Arnaldo Dimagli http://orcid.org/0000-0003-0055-8656

Umberto Benedetto http://orcid.org/0000-0002-7074-7949

Gianni D Angelini http://orcid.org/0000-0002-1753-3730

Article

Publisher ID: 60384

DOI: 10.2196/60384

PMC ID: 11217161

SO-VID: d4e7e1cd-8738-48f2-930e-ae596825ecbf

Copyright © Copyright © Tim Dong, Shubhra Sinha, Ben Zhai, Daniel Fudulu, Jeremy Chan, Pradeep Narayan, Andy Judge, Massimo Caputo, Arnaldo Dimagli, Umberto Benedetto, Gianni D Angelini. Originally published in JMIRx Med (https://med.jmirx.org)

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIRx Med, is properly cited. The complete bibliographic information, a link to the original publication on https://med.jmirx.org/, as well as this copyright and license information must be included.

Authors’ Response to Peer Reviews of “Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis”

Read this article at

Related collections

Novel Coronavirus Disease COVID-19

Most cited references 11

Can machine learning improve mortality prediction following cardiac surgery?

Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft: Improving Risk Prediction With Intraoperative Events Using Gradient Boosting.

External model validation of binary clinical risk prediction models in cardiovascular and thoracic surgery

Author and article information

Contributors

Journal

Affiliations

Author notes

Author information

Article

History

Categories

Comments

Comment on this article

Similar content 997

Most referenced authors 65