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Found 2 papers, 1 papers with code
Temporal Dependencies in Feature Importance for Time Series Predictions
Time series data introduces two key challenges for explainability methods: firstly, observations of the same feature over subsequent time steps are not independent, and secondly, the same feature can have varying importance to model predictions over time.
Diabetes Mellitus Forecasting Using Population Health Data in Ontario, Canada
We perform one of the first large-scale machine learning studies with this data to study the task of predicting diabetes in a range of 1-10 years ahead, which requires no additional screening of individuals. In the best setup, we reach a test AUC of 80. 3 with a single-model trained on an observation window of 5 years with a one-year buffer using all datasets.
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