An efficient edge/cloud medical system for rapid detection of level of consciousness in emergency medicine based on explainable machine learning models.
Edge/cloud medical system
Emergency medicine
Explainability
GCS
Machine learning
Journal
Neural computing & applications
ISSN: 0941-0643
Titre abrégé: Neural Comput Appl
Pays: England
ID NLM: 9313239
Informations de publication
Date de publication:
2023
2023
Historique:
received:
20
01
2022
accepted:
06
01
2023
medline:
8
5
2023
pubmed:
8
5
2023
entrez:
8
5
2023
Statut:
ppublish
Résumé
Emergency medicine (EM) is one of the attractive research fields in which researchers investigate their efforts to diagnose and treat unforeseen illnesses or injuries. There are many tests and observations are involved in EM. Detection of the level of consciousness is one of these observations, which can be detected using several methods. Among these methods, the automatic estimation of the Glasgow coma scale (GCS) is studied in this paper. The GCS is a medical score used to describe a patient's level of consciousness. This type of scoring system requires medical examination that may not be available with the shortage of the medical expert. Therefore, the automatic medical calculation for a patient's level of consciousness is highly needed. Artificial intelligence has been deployed in several applications and appears to have a high performance regarding providing automatic solutions. The main objective of this work is to introduce the edge/cloud system to improve the efficiency of the consciousness measurement through efficient local data processing. Moreover, an efficient machine learning (ML) model to predict the level of consciousness of a certain patient based on the patient's demographic, vital signs, and laboratory tests is proposed, as well as maintaining the explainability issue using Shapley additive explanations (SHAP) that provides natural language explanation in a form that helps the medical expert to understand the final prediction. The developed ML model is validated using vital signs and laboratory tests extracted from the MIMIC III dataset, and it achieves superior performance (mean absolute error (MAE) = 0.269, mean square error (MSE) = 0.625,
Identifiants
pubmed: 37155550
doi: 10.1007/s00521-023-08258-w
pii: 8258
pmc: PMC10015549
doi:
Types de publication
Journal Article
Langues
eng
Pagination
10695-10716Informations de copyright
© The Author(s) 2023.
Déclaration de conflit d'intérêts
Conflict of interestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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