Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction
<|reference_start|>Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction: We propose to meta-learn an a self-supervised patient trajectory forecast learning rule by meta-training on a meta-objective that directly optimizes the utility of the patient representation over the subsequent clinical outcome prediction. This meta-objective directly targets the usefulness of a representation generated from unlabeled clinical measurement forecast for later supervised tasks.   The meta-learned can then be directly used in target risk prediction, and the limited available samples can be used for further fine-tuning the model performance. The effectiveness of our approach is tested on a real open source patient EHR dataset MIMIC-III. We are able to demonstrate that our attention-based patient state representation approach can achieve much better performance for predicting target risk with low resources comparing with both direct supervised learning and pretraining with all-observation trajectory forecast.<|reference_end|>
@article{xue2024learning,
    title={Learning to Select the Best Forecasting Tasks for Clinical Outcome
  Prediction},
    author={Yuan Xue and Nan Du and Anne Mottram and Martin Seneviratne and Andrew
  M. Dai},
    journal={arXiv preprint arXiv:2407.19359},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19359},
    primaryClass={cs.LG cs.AI}
}