DEEP REPRESENTATION LEARNING FOR DETECTING SUBTLE PSYCHOLOGICAL SYMPTOMS IN MEDICAL RECORDS AND WEARABLE SENSOR DATA

Abstract

It is crucial to find early indicators of anxiety and moderate depression whenever you can to help people receive better results. Unstructured medical records and noisy data from wearable sensors often display these symptoms in intricate ways that supervised learning approaches that use big, annotated datasets have a hard time with. It is challenging to make good detection models because there isn't enough labeled data and it's hard to grasp the symptoms. Because of this, we need ways to train usable representations from a variety of data sources without having to label a lot of it by hand. Our suggested self-supervised deep representation learning architecture can work with many types of data at once, like medical records in text form and time-series data from wearable sensors. We employ masked data modeling and contrastive learning to gather the information we need to make unified embeddings that highlight hidden psychological symptom markers. To sort symptoms, big datasets without labels are utilized for pretraining, and subsequently small samples with labels are used for fine-tuning. The experimental evaluation that used a multimodal dataset demonstrated that the suggested technique is better than both the baseline supervised and unsupervised models at finding early psychological symptoms.