Protocol to process follow-up electronic medical records of peritoneal dialysis patients to train AI models
The absence of standardized protocols for integrating end-stage renal disease patient data into AI models has constrained the potential of AI in enhancing patient care. Here, we present a protocol for processing electronic medical records from 1,336 peritoneal dialysis patients with more than 10,000 follow-up records. We describe steps for environment setup and transforming records into analyzable formats. We then detail procedures for developing a directly usable dataset for training AI models to predict one-year all-cause mortality risk. For complete details on the use and execution of this protocol, please refer to Ma et al.<sup>1</sup>.
Authors
- Zixiang Wang
- Yasha Wang (ORCID: https://orcid.org/0000-0002-8026-9688)
- Yinghao Zhu (ORCID: https://orcid.org/0000-0002-2640-6477)
- Xinju Zhao (ORCID: https://orcid.org/0000-0001-8904-201X)
- Ling Wang (ORCID: https://orcid.org/0000-0002-1501-8903)
- Liantao Ma
- Tianlong Wang (ORCID: https://orcid.org/0009-0002-0498-6682)
- Junyi Gao (ORCID: https://orcid.org/0000-0002-4951-8682)
- Wen Tang (ORCID: https://orcid.org/0000-0002-6634-9008)
- Tao Wang (ORCID: https://orcid.org/0009-0000-9631-0780)
Institutions
- Xuzhou Medical College (CN)
- Peking University (CN)
- Peking University People's Hospital (CN)
- Peking University Third Hospital (CN)
- Health Data Research UK (GB)
Publication Details
- Journal
- STAR Protocols
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1016/j.xpro.2024.103335
- Primary Topic
- Dialysis and Renal Disease Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Alan Turing Institute
- National Natural Science Foundation of China
- Peking University
- Peking University Third Hospital
- Medical Research Council