M3care: Learning with missing modalities in multimodal healthcare data
Multimodal electronic health record (EHR) data are widely used in clinical applications.
Conventional methods usually assume that each sample (patient) is associated with the …
Conventional methods usually assume that each sample (patient) is associated with the …
[HTML][HTML] Effects of a personalized fitness recommender system using gamification and continuous player modeling: system design and long-term validation study
Background: Gamification and persuasive games are effective tools to motivate behavior
change, particularly to promote daily physical activities. On the one hand, studies have …
change, particularly to promote daily physical activities. On the one hand, studies have …
Recommendation with generative models
Generative models are a class of AI models capable of creating new instances of data by
learning and sampling from their statistical distributions. In recent years, these models have …
learning and sampling from their statistical distributions. In recent years, these models have …
Self-supervised pretraining and transfer learning enable\titlebreak flu and covid-19 predictions in small mobile sensing datasets
MA Merrill, T Althoff - Conference on Health, Inference, and …, 2023 - proceedings.mlr.press
Detailed mobile sensing data from phones and fitness trackers offer an opportunity to
quantify previously unmeasurable behavioral changes to improve individual health and …
quantify previously unmeasurable behavioral changes to improve individual health and …
Asynchronous federated learning for sensor data with concept drift
Federated learning (FL) involves multiple distributed devices jointly training a shared model
without any of the participants having to reveal their local data to a centralized server. Most …
without any of the participants having to reveal their local data to a centralized server. Most …