EMR-LIP: A lightweight framework for standardizing the preprocessing of longitudinal irregular data in electronic medical records
EMR-LIP streamlines the preprocessing of irregular longitudinal EMR data, offering an end-to-end solution for model-ready data creation, and has been open-sourced for collaborative refinement by the research community.
Authors
- Jiajun Qiu (ORCID: https://orcid.org/0000-0002-0972-9338)
- Jin Yin (ORCID: https://orcid.org/0000-0002-8607-091X)
- Jiawei Luo (ORCID: https://orcid.org/0000-0003-1617-3224)
- Xiaobo Zhou (ORCID: https://orcid.org/0000-0001-7191-6495)
- Xiaoyan Yang (ORCID: https://orcid.org/0000-0002-7591-7505)
- Shixin Huang (ORCID: https://orcid.org/0009-0001-7931-7056)
- Lan Lan
- Yingqiang Guo (ORCID: https://orcid.org/0000-0003-4894-2614)
- Tingqian Cao
- Shu Yang (ORCID: https://orcid.org/0000-0002-1859-6362)
Institutions
- Chongqing University of Posts and Telecommunications (CN)
- Sichuan University (CN)
- Beijing Tian Tan Hospital (CN)
- West China Hospital of Sichuan University (CN)
- The University of Texas Health Science Center (US)
- Chengdu University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2024-11-24
- DOI
- https://doi.org/10.1016/j.cmpb.2024.108521
- Citations
- 3
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- National Natural Science Foundation of China
- Sichuan University
- Sichuan Province Science and Technology Support Program
- West China Hospital, Sichuan University
- Key Research and Development Program of Sichuan Province