Protocol to process follow-up electronic medical records of peritoneal dialysis patients to train AI models
Year: 2024
Type: article
Abstract: 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. 1
Source: STAR Protocols
Authors: Tianlong Wang, Yinghao Zhu, Zixiang Wang, Wen Tang, Xinju Zhao, Tao Wang, Yasha Wang, Junyi Gao, Liantao Ma, Ling Wang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,500
Awards: G1900704574, G3372715192, G549236285
OpenAlex ID: W4403113302