Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information

• URL: http://arxiv.org/abs/2407.13429v1
• Date: Thu, 18 Jul 2024 11:54:34 GMT
• Title: Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
• Authors: Fedor Sergeev, Paola Malsot, Gunnar Rätsch, Vincent Fortuin,
• Abstract summary: Knowing which of a time series to measure and when is a key task in medicine, wearables. Inspired by conditional mutual information, we propose an approach to train acquirers end-to-end using only downstream loss.
• Score: 11.882952809819855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowing which features of a multivariate time series to measure and when is a key task in medicine, wearables, and robotics. Better acquisition policies can reduce costs while maintaining or even improving the performance of downstream predictors. Inspired by the maximization of conditional mutual information, we propose an approach to train acquirers end-to-end using only the downstream loss. We show that our method outperforms random acquisition policy, matches a model with an unrestrained budget, but does not yet overtake a static acquisition strategy. We highlight the assumptions and outline avenues for future work.

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