Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction

• URL: http://arxiv.org/abs/2407.19359v1
• Date: Sun, 28 Jul 2024 01:22:04 GMT
• Title: Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction
• Authors: Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai,
• Abstract summary: We propose to meta-learn an a self-supervised patient trajectory forecast learning rule by meta-training on a meta-objective. This meta-objective directly targets the usefulness of a representation generated from unlabeled clinical measurement forecast for later supervised tasks. The effectiveness of our approach is tested on a real open source patient EHR dataset MIMIC-III.
• Score: 25.535543983198107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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