Development and external validation of multimodal postoperative acute kidney injury risk machine learning models.

ABSTRACT:

Objectives

To develop and externally validate machine learning models using structured and unstructured electronic health record data to predict postoperative acute kidney injury (AKI) across inpatient settings.

Materials and methods

Data for adult postoperative admissions to the Loyola University Medical Center (2009-2017) were used for model development and admissions to the University of Wisconsin-Madison (2009-2020) were used for validation. Structured features included demographics, vital signs, laboratory results, and nurse-documented scores. Unstructured text from clinical notes were converted into concept unique identifiers (CUIs) using the clinical Text Analysis and Knowledge Extraction System. The primary outcome was the development of Kidney Disease Improvement Gl

SUBMITTER: Karway GK 

PROVIDER: S-EPMC10746378 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature