Depression treatment is effective for approximately 50-60% of patients who receive treatment, but the probability of a successful response is typically unknown before treatment begins. As a result, depression treatment is routinely delivered in a trial-and-error fashion until a satisfactory response is achieved. Our objective is to provide a personalized prognosis by applying ensemble machine learning techniques to discover novel, non-linear combinations of multiple weak predictors that collectively yield accurate predictions of treatment outcome. This statistical approach considers many prediction variables simultaneously and iteratively constructs a complex prediction model that often dramatically outperforms traditional statistical methods.
Aim 1 is to apply stochastic gradient boosted decision trees to predict response to citalopram using archival data from the STAR*D clinical trial. In preliminary analyses, we randomly selected 1223 patients to train the model and another 407 patients to independently test the model (a 75-25 split), with tuning parameters selected by cross-validation to minimize log-loss. The resulting prediction on the independent test sample was superior to the no-information rate (p < 0.001), with an overall predictive accuracy of 66%. Although this level of prediction is significantly better than a no information model, we plan to improve the model's prognostication by 1) adding features that capture the ?pharmacological noise? of concurrent (non- study) medication use and 2) updating model predictions based on early signs of response.
Aim 2 is to use a similar machine learning approach to examine response to internet-based CBT for depression. Internet-based treatments for depression are growing in popularity, provide efficient access to health care, reduce treatment costs, and have good evidence for treatment efficacy. Importantly, we have a large dataset (N = 1,013) within which to develop treatment-matching algorithms that predict treatment response based on patient attributes. Study Impact: The overarching goal of this project is to use machine learning methods to develop treatment matching algorithms. In the long term, we can envision a system that evaluates a patient on a number of important predictor variables and provides a personalized probability of treatment success. These probabilities would then be used to guide treatment selection or modify current treatment if a poor response is predicted. Developing algorithms that successfully predict whether a particular form of treatment is likely to be successful for a patient with a given set of attributes would be a tremendous step towards efficient and personalized depression treatment.
Depression treatment is effective for roughly half of the patients who receive treatment, but it is usually unknown how a specific patient with a particular set of attributes will respond to a given treatment. Our objective is to provide a personalized prognosis by applying ensemble machine learning techniques to discover novel combinations of multiple weak predictors that collectively yield accurate predictions of treatment outcome. Developing algorithms that successfully predict whether a particular form of treatment is likely to be successful for a patient would be a tremendous step towards efficient and personalized depression treatment.
| Dainer-Best, Justin; Shumake, Jason D; Beevers, Christopher G (2018) Positive imagery training increases positive self-referent cognition in depression. Behav Res Ther 111:72-83 |
