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Literature DB >> 24615859

Risk prediction with machine learning and regression methods.

Ewout W Steyerberg¹, Tjeerd van der Ploeg, Ben Van Calster.

Abstract

This is a discussion of issues in risk prediction based on the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler.

Keywords: Machine learning; Prediction; Regression

Mesh：

Year: 2014 PMID： 24615859 DOI： 10.1002/bimj.201300297

Source DB: PubMed Journal: Biom J ISSN： 0323-3847 Impact factor: 2.207

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Cited

15 in total

1. A nonparametric updating method to correct clinical prediction model drift.

Authors: Sharon E Davis; Robert A Greevy; Christopher Fonnesbeck; Thomas A Lasko; Colin G Walsh; Michael E Matheny
Journal: J Am Med Inform Assoc Date: 2019-12-01 Impact factor: 4.497

2. Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality.

Authors: Sharon E Davis; Thomas A Lasko; Guanhua Chen; Michael E Matheny
Journal: AMIA Annu Symp Proc Date: 2018-04-16

3. Prognostic models will be victims of their own success, unless….

Authors: Matthew C Lenert; Michael E Matheny; Colin G Walsh
Journal: J Am Med Inform Assoc Date: 2019-12-01 Impact factor: 4.497

Review 4. Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care.

Authors: Brandon Foreman
Journal: Neurotherapeutics Date: 2020-04 Impact factor: 7.620

5. A Machine Learning Classifier Improves Mortality Prediction Compared With Pediatric Logistic Organ Dysfunction-2 Score: Model Development and Validation.

Authors: Remi D Prince; Alireza Akhondi-Asl; Nilesh M Mehta; Alon Geva
Journal: Crit Care Explor Date: 2021-05-17

Review 6. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

Authors: Benjamin A Goldstein; Ann Marie Navar; Rickey E Carter
Journal: Eur Heart J Date: 2017-06-14 Impact factor: 29.983

Risk prediction with machine learning and regression methods.

1. A nonparametric updating method to correct clinical prediction model drift.

2. Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality.

3. Prognostic models will be victims of their own success, unless….

Review 4. Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care.

5. A Machine Learning Classifier Improves Mortality Prediction Compared With Pediatric Logistic Organ Dysfunction-2 Score: Model Development and Validation.

Review 6. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

7. Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients.

8. A cardiovascular risk prediction model for older people: Development and validation in a primary care population.

9. Calibration drift in regression and machine learning models for acute kidney injury.

10. Cardiovascular risk and aging: the need for a more comprehensive understanding.