Prediction of Glycemic Event in Emergency Section Patients Using Machine Learning

Effective diabetes management is crucial to pre-venting serious complications in patients. This study employs machine learning techniques to analyze data from 11 Spanish hospital emergency departments, with the goal of improving the quality of life for individuals with type I and type II diabetes. Our model demonstrates a prediction accuracy of up to 95% for hypo- and hyperglycemia, and highlights significant insights into antidiabetic treatments. The results were validated using a decision tree and correlation plots, showing relationships between key variables. This approach shows potential for large-scale prediction of glycemic events and provides valuable support for clinical decision-making.

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Published
2024-11-18
DOI
https://doi.org/10.1109/healthcom60970.2024.10880819
Citations
3
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
2.12
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Prediction of Glycemic Event in Emergency Section Patients Using Machine Learning

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Prediction of Glycemic Event in Emergency Section Patients Using Machine Learning

Alessio Botta, L García, Sayna Rotbei, Giuseppe Fico, Ricardo Mesanza Forés, Ana Sánchez, Beatriz Merino‐Barbancho, Aitor Odiaga Andicoechea, Andrea Irimia, Arturo Corbatón Anchuelo, Ricardo Muñoz Albert, Hania Tourab, Raquel Piñero Panadero, Fahd Beddar Chaib, Pablo Matías Soler, Esther Ruescas Esculano, Mireia Cramp Vinceixo, Laura Mariel Matus, María Ángeles San Martín Díezv, Rosana Soriano Barrónix
article en
3 citations

Abstract

Effective diabetes management is crucial to pre-venting serious complications in patients. This study employs machine learning techniques to analyze data from 11 Spanish hospital emergency departments, with the goal of improving the quality of life for individuals with type I and type II diabetes. Our model demonstrates a prediction accuracy of up to 95% for hypo- and hyperglycemia, and highlights significant insights into antidiabetic treatments. The results were validated using a decision tree and correlation plots, showing relationships between key variables. This approach shows potential for large-scale prediction of glycemic events and provides valuable support for clinical decision-making.

Hospital Clínico San Carlos (ES), Hospital de Basurto (ES), Hospital de La Ribera (ES), Hospital de Galdakao (ES), Hospital Universitario Central de Asturias (ES), Clinica Universidad de Navarra (ES), Hospital Universitario del Vinalopó (ES), Hospital San Pedro (ES), Hospital Universitario de Fuenlabrada (ES), Hospital Universitari Joan XXIII de Tarragona (ES), University of Naples Federico II (IT)
Zero hunger
Openalex Percentile: Top 9%
Artificial Intelligence in Healthcare
2.12
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