Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy
This work addresses automated depression detection for users via wearable devices, but it is incremental as it applies existing transfer learning methods to a specific healthcare application.
The paper tackled the problem of limited data for machine learning in healthcare by using transfer learning from a secondary dataset to deploy a real-time depression screening tool based on actigraphy data, achieving a mean accuracy of 0.96 in cross-validation.
Automated depression screening and diagnosis is a highly relevant problem today. There are a number of limitations of the traditional depression detection methods, namely, high dependence on clinicians and biased self-reporting. In recent years, research has suggested strong potential in machine learning (ML) based methods that make use of the user's passive data collected via wearable devices. However, ML is data hungry. Especially in the healthcare domain primary data collection is challenging. In this work, we present an approach based on transfer learning, from a model trained on a secondary dataset, for the real time deployment of the depression screening tool based on the actigraphy data of users. This approach enables machine learning modelling even with limited primary data samples. A modified version of leave one out cross validation approach performed on the primary set resulted in mean accuracy of 0.96, where in each iteration one subject's data from the primary set was set aside for testing.