@inproceedings{f9b2ded6c0964355ad20b0a80b4a955d,
title = "Embracing Disease Progression with a Learning System for Real World Evidence Discovery",
abstract = "Electronic Health Records (EHRs) have been widely used in healthcare studies recently, such as the analyses for patient diagnostic outcome and understanding of disease progression. EHR is a treasure for researchers who conduct the Real-World study to discovering Real-World Evidence (RWE). In this paper, we design an end-to-end learning system for disease states discovery based on a data-driven strategy. A large-scale proprietary EHR data mart containing about 55 million patients with over 20 billion data records is used for data extraction and analysis. Given a disease of interest, researchers could easily obtain the hidden disease states. Once our system were operational, biomedical researchers could get the results for downstream analyses such as disease prediction, drug design and outcome analyses.",
keywords = "Biomedical analyses, Disease progression, EHRs, Real World Evidence discovery",
author = "Zefang Tang and Lun Hu and Xu Min and Yuan Zhang and Jing Mei and Kenney Ng and Shaochun Li and Pengwei Hu and Zhuhong You",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th International Conference on Intelligent Computing, ICIC 2020 ; Conference date: 02-10-2020 Through 05-10-2020",
year = "2020",
doi = "10.1007/978-3-030-60802-6_46",
language = "英语",
isbn = "9783030608019",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "524--534",
editor = "De-Shuang Huang and Kang-Hyun Jo",
booktitle = "Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings",
}