(Proper credits should be given to the speakers if the slides are reproduced or published)
Title: How can a wristband with AI help improve brain health?
Speaker: Dr. Rosalind Picard, MIT Media Lab
PDF Slides.
Medicine has not solved several of the greatest health challenges; however, engineering and AI can help in significant new ways. For example, mental health problems such as depression have continued to grow and are on track to be the #1 disease burden globally by 2030; yet, doctors do not understand what causes or precipitates depression, or how to forecast it. Wristbands and other mobile/wearable data-providing devices can now measure continuous physiology and activity/sleep data that are analyzed for detecting subtly changing patterns related to changing health, and providing early forecasts. What are some of these patterns telling us about neurological activity, mood, stress and health and how accurately? This talk will highlight some of the latest results from our team at MIT, and also touch on some of the scientific and ethical challenges, in advancing better brain health for everyone.
Title: Intelligent Monitoring of Individual and Population Mental Health using Social Media Data
Speaker: Dr. Jiebo Luo, University of Rocheste
Mental illness is becoming a major plague in modern societies and poses challenges to the capacity of current public health systems worldwide. With the widespread adoption of social media and mobile devices, and rapid advances in artificial intelligence, a unique opportunity arises for tackling mental health problems. Previously, we investigated how users' online social activities and physiological signals detected through ubiquitous smartphone sensors can be utilized in realistic scenarios for monitoring their mental health states. We can extract a suite of multimodal time-series signals, using modern computer vision and signal processing techniques, from recruited participants while they are immersed in online social media that elicit or reflect emotions and emotion transitions. We then use machine learning techniques to build a model that establishes the connection between mental states and the extracted multimodal signals. Recently, we study how the COVID-19 pandemic has severely affected people's mental health at scale. We chose social media as our main data resource and created by far the largest English Twitter depression dataset containing 2,575 distinct identified depression users with their past tweets. To examine the effect of depression on people's Twitter language, we train three transformer-based depression classification models on the dataset, evaluate their performance with progressively increased training sizes, and compare the model's "tweet chunk"-level and user-level performances. Furthermore, inspired by psychological studies, we create a fusion classifier that combines deep learning model scores with psychological text features and users' demographic information and investigate these features' relations to depression signals. Finally, we demonstrate our model's capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic.
Title: TBA
Speaker: Dr. Chris Wright, NHS Scotland
TBA.