Creating sparser prediction models of treatment outcome in depression: a proof-of-concept study using simultaneous feature selection and hyperparameter tuning

Rost, Nicolas, Bruckl, Tanja M, Koutsouleris, Nikolaos, Binder, Elisabeth B and Mueller-Myhsok, Bertram ORCID: 0000-0002-0719-101X (2022) Creating sparser prediction models of treatment outcome in depression: a proof-of-concept study using simultaneous feature selection and hyperparameter tuning. BMC MEDICAL INFORMATICS AND DECISION MAKING, 22 (1). 181-.

Access the full-text of this item by clicking on the Open Access link.

Official URL: http://dx.doi.org/10.1186/s12911-022-01926-2

Abstract

<h4>Background</h4>Predicting treatment outcome in major depressive disorder (MDD) remains an essential challenge for precision psychiatry. Clinical prediction models (CPMs) based on supervised machine learning have been a promising approach for this endeavor. However, only few CPMs have focused on model sparsity even though sparser models might facilitate the translation into clinical practice and lower the expenses of their application.<h4>Methods</h4>In this study, we developed a predictive modeling pipeline that combines hyperparameter tuning and recursive feature elimination in a nested cross-validation framework. We applied this pipeline to a real-world clinical data set on MDD treatment response and to a second simulated data set using three different classification algorithms. Performance was evaluated by permutation testing and comparison to a reference pipeline without nested feature selection.<h4>Results</h4>Across all models, the proposed pipeline led to sparser CPMs compared to the reference pipeline. Except for one comparison, the proposed pipeline resulted in equally or more accurate predictions. For MDD treatment response, balanced accuracy scores ranged between 61 and 71% when models were applied to hold-out validation data.<h4>Conclusions</h4>The resulting models might be particularly interesting for clinical applications as they could reduce expenses for clinical institutions and stress for patients.

Item Type:	Article
Uncontrolled Keywords:	Major depressive disorder, Treatment outcome, Predictive modeling, Feature selection, Precision psychiatry, Supervised learning
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health
Depositing User:	Symplectic Admin
Date Deposited:	07 Sep 2022 09:10
Last Modified:	18 Jan 2023 20:45
DOI:	10.1186/s12911-022-01926-2
Open Access URL:	https://doi.org/10.1186/s12911-022-01926-2
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3163727