Building and Prospectively Evaluating a Prediction Model to Forecast Urgent Dialysis Needs across Four Tertiary Hospitals.

ABSTRACT:

Introduction

Urgent dialysis is labor-intensive and expensive because it requires specialized nursing staff. Most hospitals schedule a fixed number of nurses daily for urgent dialysis needs, but daily dialysis demand fluctuates, leading to inefficiencies.

Methods

We developed statistical, machine learning, and deep learning models to predict the next 7 days' dialysis needs. Our study included a retrospective (April 1, 2018, to March 31, 2023) and prospective component (November 1 to 30, 2023, and May 31 to June 27, 2024) across four hospitals (hospital A for one hospital and hospital B for three hospitals combined). To avoid model over-fitting, we divided our data into three sets: training, testing, and validation. The latter was performed prospectively during two silent dep

SUBMITTER: Lim B 

PROVIDER: S-EPMC12700581 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature