AI-Driven Prediction of Dialysis Outcome With EHR

Predicting Clinical Outcomes in Dialysis Patients Using Electronic Health Records: An AI-Based Approach

This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for outcome of dialysis patients, leveraging multimodal health data.

This study aims to develop an AI-assisted model to predict clinical outcomes in dialysis patients, focusing on both primary outcomes (e.g., mortality) and intermediate outcomes (e.g., anemia, blood pressure, nutritional status, and calcium-phosphate metabolism). The study will utilize patients' EHR data, including laboratory test results, medical history, dialysis treatment information, and clinical observations, to predict these health outcomes. The goal is to improve early identification of at-risk patients, enabling better clinical decision-making and personalized care strategies.

Inclusion Criteria:

1. Patients who have been undergoing dialysis (either hemodialysis or peritoneal dialysis) for at least 3 months.
2. Complete and accessible EHR data, including medical history, laboratory test results, dialysis treatment details, and clinical observations.
3. Participants must provide informed consent for the use of their health data for research purposes.

Exclusion Criteria:

1. Patients with incomplete or missing critical EHR data, including medical history, laboratory results, dialysis data, or treatment details necessary for the study.
2. Patients who have been on dialysis for less than 3 months, to ensure stable data for outcome prediction.