Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models. Yang, Z., Liu, W., Berlowitz, D., & Yu, H. December, 2022. arXiv:2212.12067 [cs]![Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@misc{yang_enhancing_2022,
title = {Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models},
url = {http://arxiv.org/abs/2212.12067},
doi = {10.48550/arXiv.2212.12067},
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.},
urldate = {2023-02-19},
publisher = {arXiv},
author = {Yang, Zhichao and Liu, Weisong and Berlowitz, Dan and Yu, Hong},
month = dec,
year = {2022},
note = {arXiv:2212.12067 [cs]},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning},
}
Downloads: 0
{"_id":"NstKtXEjpTQ6WqaSX","bibbaseid":"yang-liu-berlowitz-yu-enhancingthepredictionofdiseaseoutcomesusingelectronichealthrecordsandpretraineddeeplearningmodels-2022","author_short":["Yang, Z.","Liu, W.","Berlowitz, D.","Yu, H."],"bibdata":{"bibtype":"misc","type":"misc","title":"Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models","url":"http://arxiv.org/abs/2212.12067","doi":"10.48550/arXiv.2212.12067","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.","urldate":"2023-02-19","publisher":"arXiv","author":[{"propositions":[],"lastnames":["Yang"],"firstnames":["Zhichao"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Weisong"],"suffixes":[]},{"propositions":[],"lastnames":["Berlowitz"],"firstnames":["Dan"],"suffixes":[]},{"propositions":[],"lastnames":["Yu"],"firstnames":["Hong"],"suffixes":[]}],"month":"December","year":"2022","note":"arXiv:2212.12067 [cs]","keywords":"Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning","bibtex":"@misc{yang_enhancing_2022,\n\ttitle = {Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models},\n\turl = {http://arxiv.org/abs/2212.12067},\n\tdoi = {10.48550/arXiv.2212.12067},\n\tabstract = {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.},\n\turldate = {2023-02-19},\n\tpublisher = {arXiv},\n\tauthor = {Yang, Zhichao and Liu, Weisong and Berlowitz, Dan and Yu, Hong},\n\tmonth = dec,\n\tyear = {2022},\n\tnote = {arXiv:2212.12067 [cs]},\n\tkeywords = {Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning},\n}\n\n","author_short":["Yang, Z.","Liu, W.","Berlowitz, D.","Yu, H."],"key":"yang_enhancing_2022","id":"yang_enhancing_2022","bibbaseid":"yang-liu-berlowitz-yu-enhancingthepredictionofdiseaseoutcomesusingelectronichealthrecordsandpretraineddeeplearningmodels-2022","role":"author","urls":{"Paper":"http://arxiv.org/abs/2212.12067"},"keyword":["Computer Science - Artificial Intelligence","Computer Science - Computers and Society","Computer Science - Machine Learning"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"misc","biburl":"http://fenway.cs.uml.edu/papers/pubs-all.bib","dataSources":["TqaA9miSB65nRfS5H"],"keywords":["computer science - artificial intelligence","computer science - computers and society","computer science - machine learning"],"search_terms":["enhancing","prediction","disease","outcomes","using","electronic","health","records","pretrained","deep","learning","models","yang","liu","berlowitz","yu"],"title":"Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models","year":2022}