Liu, Qiang; Salanti, Georgia; De Crescenzo, Franco; Ostinelli, Edoardo Giuseppe; Li, Zhenpeng; Tomlinson, Anneka; Cipriani, Andrea; Efthimiou, Orestis (2022). Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression. BMC psychiatry, 22(1), p. 337. BioMed Central 10.1186/s12888-022-03986-0
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BACKGROUND
The debate of whether machine learning models offer advantages over standard statistical methods when making predictions is ongoing. We discuss the use of a meta-learner model combining both approaches as an alternative.
METHODS
To illustrate the development of a meta-learner, we used a dataset of 187,757 people with depression. Using 31 variables, we aimed to predict two outcomes measured 60 days after initiation of antidepressant treatment: severity of depressive symptoms (continuous) and all-cause dropouts (binary). We fitted a ridge regression and a multi-layer perceptron (MLP) deep neural network as two separate prediction models ("base-learners"). We then developed two "meta-learners", combining predictions from the two base-learners. To compare the performance across the different methods, we calculated mean absolute error (MAE, for continuous outcome) and the area under the receiver operating characteristic curve (AUC, for binary outcome) using bootstrapping.
RESULTS
Compared to the best performing base-learner (MLP base-learner, MAE at 4.63, AUC at 0.59), the best performing meta-learner showed a 2.49% decrease in MAE at 4.52 for the continuous outcome and a 6.47% increase in AUC at 0.60 for the binary outcome.
CONCLUSIONS
A meta-learner approach may effectively combine multiple prediction models. Choosing between statistical and machine learning models may not be necessary in practice.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM) 04 Faculty of Medicine > Medical Education > Institute of General Practice and Primary Care (BIHAM) |
UniBE Contributor: |
Salanti, Georgia, Efthimiou, Orestis |
Subjects: |
600 Technology > 610 Medicine & health 300 Social sciences, sociology & anthropology > 360 Social problems & social services |
ISSN: |
1471-244X |
Publisher: |
BioMed Central |
Funders: |
[4] Swiss National Science Foundation |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
18 May 2022 09:31 |
Last Modified: |
05 Dec 2022 16:19 |
Publisher DOI: |
10.1186/s12888-022-03986-0 |
PubMed ID: |
35578254 |
Uncontrolled Keywords: |
Depression Dropout Machine learning PHQ-9 Statistical model |
BORIS DOI: |
10.48350/170101 |
URI: |
https://boris.unibe.ch/id/eprint/170101 |
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