Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish.

ABSTRACT:

Introduction

Accurate identification of graft loss in Electronic Medical Records of kidney transplant recipients is essential but challenging due to inconsistent and not mandatory International Classification of Diseases (ICD) codes. We developed and validated Natural Language Processing (NLP) and machine learning models to classify the status of kidney allografts in unstructured text in EMRs written in Spanish.

Methods

We conducted a retrospective cohort of 2712 patients transplanted between July 2008 and January 2023, analyzing 117,566 unstructured medical records. NLP involved text normalization, tokenization, stopwords removal, spell-checking, elimination of low-frequency words and stemming. Data was split in training, validation and test sets. Data balance was performed

SUBMITTER: Garcia-Lopez A 

PROVIDER: S-EPMC12061128 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature