Optimizing prediction of response to antidepressant medications using machine learning and environmental data
Spinrad, A., Darki-Morag, S. & Taliaz, D.
Optimizing prediction of response to antidepressant medications using machine learning and environmental data. Eur. Psychiatry 64, S755–S755 (2021).
![1024px-Nature_journal_logo 1](https://predictix.ai/wp-content/uploads/2021/08/1024px-Nature_journal_logo-1.png)
Summary
The current treatment for Major depression (MDD) consists mostly of trial and error, with antidepressants (AD) estimated to have response rates of 42%-53%. Combining clinical and demographic factors with feature selection techniques and machine-learning algorithms may contribute to understanding MDD’s multi factor complexity. Selected machine learning models for 5 antidepressants achieved a cumulative average accuracy of 60.12%.
Discover more about PREDICTIX Digital decision support tool.
Abstract
MDD is complex and multifactorial, presenting a major challenge to tailoring medication for each patient. Currently, treatment for MDD is mostly trial-and-error, with an estimated response rate of 42%-53% for antidepressants.
Objectives
We used a data-driven approach to analyze a combination of clinical and demographic factors in order to develop an accurate predictor of response to a panel of antidepressants and optimize treatment selection.
Methods
We analyzed the response patterns of patients to several antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study and the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results and confirm the algorithm’s external generalizability outside of its training groups, we assessed its capacity to predict individualized antidepressant responses on a separate validation and test sets consisting of 1,021 patients overall from both studies.
Results
The algorithm’s ML prediction models achieved an average accuracy of 0.6416 (64.16%, SD 4.4) across the analyzed medications, and a cumulative accuracy of 0.6012 (60.12%), AUC of 0.601, sensitivity of 0.6034 (60.34%) and specificity of 0.599 (59.9%).
Conclusions
In this study, ML was applied to accumulating data derived from large studies in order to make a significant improvement in treating depression. Such predictive models might help doctors make better prescription decisions by analyzing a large amount of combinatorial data immediately on the spot, potentially enabling them to prescribe the right antidepressant medication earlier.