Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
<|reference_start|>Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information: 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.<|reference_end|>
@article{sergeev2024towards,
    title={Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing
  Conditional Mutual Information},
    author={Fedor Sergeev, Paola Malsot, Gunnar R"atsch, Vincent Fortuin},
    journal={arXiv preprint arXiv:2407.13429},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13429},
    primaryClass={cs.LG cs.AI}
}