Optimizing Existing Mental Health Screening Methods in a Dementia Screening and Risk Factor App: Observational Machine Learning Study (Preprint)

Abstract

BACKGROUND Mindset4Dementia is an app that aims to improve dementia screening by assessing cognition and risk factors. It considers important clinical risk factors, including prodromal symptoms, mental health disorders, and differential diagnoses of dementia. The PHQ-9 and GAD-7 are widely validated, and commonly used scales used in screening for depression and anxiety disorders respectively. Shortened versions of both (PHQ-2/GAD-2) have been produced. OBJECTIVE We sought to develop a method that maintained the brevity of these shorter questionnaires while maintaining the better precision of the original questionnaires METHODS Single questions were designed to encompass symptoms covered in the original questionnaires. Answers to these questions were combined with the PHQ-2/GAD-2 and anonymized risk factors collected by Mindset4Dementia. Machine learning models were trained to use these single questions in combination with data already collected by the app - age, response to a joke and reporting of functional impairment to predict binary and continuous outcomes as measured by the PHQ-9/GAD-7. Our model was developed with a training dataset using ten-fold cross-validation and a hold-out testing datasets and compared to results from using the shorter questionnaires (PHQ-2/GAD-2) alone to benchmark performance. RESULTS We were able to achieve superior performance in predicting PHQ-9/GAD-7 screening cut-offs than the PHQ-2 (difference In AUC 0.04, 95% CI 0.00 – 0.08, P = 0.02) but not to GAD-2 (difference in AUC 0.00, 95% CI -0.02 – 0.03, P = 0.42). In regression models we were able to accurately predict total questionnaire scores; PHQ-9 (R2 = 0.655, MAE = 2.267), GAD-7 (R2 = 0.837, MAE = 1.780). CONCLUSIONS We have developed a short screening tool for affective disorders with superior or equivalent performance to well established methods.