• woman checking fitness and health tracking

MAPS: Mobile Assessment for the Prediction of Suicide

  • Goal: Prediction of Suicidal Thoughts and Behaviors in Youth Using Intensive mobile sensing
  • Hypothesis Driven Data analysis
    • Interpretable features (e.g., Sleep duration, Sentiment in language, Music choice, Frequency of social contact)
    • Dynamic factor analysis to detect underlying temporal structure of time series data
    • Multilevel regression techniques (i.e., autocorrelation/cross-correlation models)
    • Mixed Markov models for predicting  state-switching (i.e., changes in suicide risk occurring within a person over time)
  • Empirically-Driven Data Analysis (Machine Learning)
    • Will explore all available data (hypothesized and non-hypothesized) using deep learning models
    • Computational feature representation to extract features understandable by the computer from the raw input streams
    • Concurrent fusion to represent co-occurring visual, acoustic, verbal, and behavioral features (i.e., to model synchronized behaviors) and
    •  Temporal fusion to model the temporal patterns and contingencies between the multimodal behaviors (i.e., to model asynchronized behaviors such as changes in suicidal risk)
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