Back to Search
Start Over
Combining machine learning algorithms for prediction of antidepressant treatment response
- Source :
- Acta Psychiatrica Scandinavica
- Publication Year :
- 2020
- Publisher :
- Wiley, 2020.
-
Abstract
- Objectives Predictors for unfavorable treatment outcome in major depressive disorder (MDD) applicable for treatment selection are still lacking. The database of a longitudinal multicenter study on 1079 acutely depressed patients, performed by the German research network on depression (GRND), allows supervised and unsupervised learning to further elucidate the interplay of clinical and psycho‐sociodemographic variables and their predictive impact on treatment outcome phenotypes. Experimental Procedures Treatment response was defined by a change of HAM‐D 17‐item baseline score ≥50% and remission by the established threshold of ≤7, respectively, after up to eight weeks of inpatient treatment. After hierarchical symptom clustering and stratification by treatment subtypes (serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotic, and lithium augmentation), prediction models for different outcome phenotypes were computed with random forest in a cross‐center validation design. In total, 88 predictors were implemented. Results Clustering revealed four distinct HAM‐D subscores related to emotional, anxious, sleep, and appetite symptoms, respectively. After feature selection, classification models reached moderate to high accuracies up to 0.85. Highest accuracies were observed for the SSRI and TCA subgroups and for sleep and appetite symptoms, while anxious symptoms showed poor predictability. Conclusion Our results support a decisive role for machine learning in the management of antidepressant treatment. Treatment‐ and symptom‐specific algorithms may increase accuracies by reducing heterogeneity. Especially, predictors related to duration of illness, baseline depression severity, anxiety and somatic symptoms, and personality traits moderate treatment success. However, prospectives application of machine learning models will be necessary to prove their value for the clinic.
- Subjects :
- affective disorders
medicine.medical_treatment
antidepressives
Feature selection
Machine learning
computer.software_genre
Machine Learning
03 medical and health sciences
0302 clinical medicine
medicine
Humans
Big Five personality traits
Antipsychotic
Depression (differential diagnoses)
Depressive Disorder, Major
business.industry
Original Articles
medicine.disease
Antidepressive Agents
030227 psychiatry
Psychiatry and Mental health
Treatment Outcome
classification
Major depressive disorder
Anxiety
Unsupervised learning
Antidepressant
Original Article
Artificial intelligence
medicine.symptom
business
Algorithm
computer
Algorithms
Selective Serotonin Reuptake Inhibitors
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 16000447 and 0001690X
- Volume :
- 143
- Database :
- OpenAIRE
- Journal :
- Acta Psychiatrica Scandinavica
- Accession number :
- edsair.doi.dedup.....8fcf98130369acff378de52b30ab0e74