Predicting anxiety treatment outcomes with machine learning

Abstract

Youth anxiety disorders are highly prevalent and associated with considerable concurrent functional impairments. According to the State of the World’s Children report, 13% of youth between 10 and 19 years old have a diagnosed mental health disorder, 40% of which are anxious and depressive disorders. In a typical longitudinal anxiety clinical study, many explanatory variables are observed in a few patients. As patients drop or miss appointments, collected data has a high missing rate in explanatory and predicted variables. We suggest using machine learning methods to improve understanding of treatments and prediction of outcomes in such studies. We propose machine learning-based imputation for understanding youth anxiety data containing features with high missing rates. In the dataset used, the missing rate of features is up to 80%, making them impossible to use in traditional analysis. Our results show that the proposed iterative imputation with a bag of elastic net regressions imputes missing data better than traditional imputation methods and allow for the best prediction result. We investigate imputation and prediction performance change when using jointly data from multiple studies, where each study has a different bias and missing rate. Leveraging joint dataset allows for predicting the therapy outcome in longitudinal studies with few patients. Additionally, we can now impute or predict features and diagnoses not reported by the clinical study. In conducted experiments, pooling data from nine different studies resulted in 9.3% smaller imputation and 33% lower prediction errors, respectively. Results have higher confidence than when studies are considered separately. We also explored the performance of imputation and prediction in the domain adaptation case of withdrawn patients, in which 50% improvement is obtained when data from all studies are used to impute and train the model.