Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models

Zhichao Yang; Weisong Liu; Dan Berlowitz; Hong Yu

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arXiv · 2212.12067

Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models

Zhichao Yang·Weisong Liu·Dan Berlowitz·Hong Yu

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Abstract

Question: Can an encoder-decoder architecture pretrained on a large dataset of longitudinal electronic health records improves patient outcome predictions? Findings: In this prognostic study of 6.8 million patients, our denoising sequence-to-sequence prediction model of multiple outcomes outperformed state-of-the-art models scuh pretrained BERT on a broad range of patient outcomes, including intentional self-harm and pancreatic cancer. Meaning: Deep bidirectional and autoregressive representation improves patient outcome prediction.

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Zhichao Yang, Weisong Liu, Dan Berlowitz, Hong Yu. 2022-12-22. Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models. https://arxiv.org/abs/2212.12067

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