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Related Experiment Video

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Simulating Impacts of Ice Storms on Forest Ecosystems

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Published on: June 30, 2020

Making complex prediction rules applicable for readers: Current practice in random forest literature and

Anne-Laure Boulesteix¹, Silke Janitza¹, Roman Hornung¹

¹Institute for Medical Information Processing, Biometry and Epidemiology, LMU Munich, Munich, Germany.

Biometrical Journal. Biometrische Zeitschrift

|August 3, 2018

Summary

Many complex prediction rules from machine learning are not applicable by readers. This study surveyed random forest prediction rules, finding few were directly usable, highlighting the need for better reporting standards.

Keywords:

logistic regression machine learning prediction rule reproducibility reproducible research

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Area of Science:

Medical and Health Sciences
Computational Biology
Biostatistics

Background:

Prediction rules are essential for clinical decision-making.
Simple prediction rules (e.g., logistic regression) are generally applicable.
Complex prediction rules from machine learning pose challenges for reader application.

Purpose of the Study:

To survey the applicability of prediction rules developed using the random forest algorithm.
To identify barriers to the application of complex prediction rules.
To provide recommendations for improving the applicability of such rules.

Main Methods:

Survey of articles published in PLOS ONE (2014-2015) in medical and health sciences.
Focused on prediction rules developed using the random forest algorithm.
Assessed rule applicability and reproducibility.

Main Results:

Only 2 out of 30 surveyed papers presented directly applicable prediction rules.
Information for 8 additional rules was obtained by contacting authors.
Author non-response and other issues hampered rule applicability in most cases.

Conclusions:

Complex prediction rules, particularly those from machine learning, often lack direct applicability.
Reproducibility and clear reporting are crucial for ensuring prediction rule usability.
Recommendations are provided to enhance the applicability of future prediction rules.