Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909270711205888 |
|---|---|
| author | Xue, Yuan Du, Nan Mottram, Anne Seneviratne, Martin Dai, Andrew M. |
| author_facet | Xue, Yuan Du, Nan Mottram, Anne Seneviratne, Martin Dai, Andrew M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19359 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction Xue, Yuan Du, Nan Mottram, Anne Seneviratne, Martin Dai, Andrew M. Machine Learning Artificial Intelligence 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. |
| title | Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.19359 |