ISSN: 2161-119X

Otolaryngology: Open Access
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  • Mini Review   
  • Otolaryngol (Sunnyvale),
  • DOI: 10.4172/2161-119X.1000501

Analysis of Nursing Safety Incident Characteristics Using Deep LearningBased Medical Data Association Rules Method in ENT Surgery

David Kohrman*
Department of Pathology, University of state capital, Australia
*Corresponding Author : David Kohrman, Department of Pathology, University of state capital, Australia, Email: david.kohrman32@gmail.com

Received Date: Jan 03, 2023 / Published Date: Jan 30, 2023

Abstract

Otolaryngology is a fairly current condition, and complications including infection and significant bleeding constantly be during surgery, which pose a serious threat to the cases' mortality. Exploring the distinctive characteristics of postoperative nursing safety events in cases who have experienced otolaryngology surgery and comprehending the distinctive features of postoperative nursing safety events in otolaryngology surgery cases are of utmost significance frequentness of postoperative safety nursing incidents were linked by this study's preoperative safety protection for 385 convalescents. According to this study, the main factors impacting postoperative care are erected lesions (95.0 C19.365 –21.038), the treatment period (95.0 CI7.147 –20.275), during hospitalization (95.0 CI8.918 –24.237), antibiotic use (95.0 CI8.163-21.739), and hypertension (95.0 CI7.926-22.385). Using the association rule system to assay and control the major threat

Citation: Kohrman D (2023) Analysis of Nursing Safety Incident CharacteristicsUsing Deep Learning-Based Medical Data Association Rules Method in ENTSurgery. Otolaryngol (Sunnyvale) 13: 501. Doi: 10.4172/2161-119X.1000501

Copyright: © 2023 Kohrman D. This is an open-access article distributed underthe terms of the Creative Commons Attribution License, which permits unrestricteduse, distribution, and reproduction in any medium, provided the original author andsource are credited.

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