دورية أكاديمية
أعرض في EDS

Using Artificial Neural Networks to Predict Intra-Abdominal Abscess Risk Post-Appendectomy.

التفاصيل البيبلوغرافية
العنوان: Using Artificial Neural Networks to Predict Intra-Abdominal Abscess Risk Post-Appendectomy.
المؤلفون: Alramadhan MM; From the Division of Infectious Diseases, Department of Pediatrics, UTHealth Houston McGovern Medical School, Houston, TX., Al Khatib HS; From the Division of Infectious Diseases, Department of Pediatrics, UTHealth Houston McGovern Medical School, Houston, TX., Murphy JR; From the Division of Infectious Diseases, Department of Pediatrics, UTHealth Houston McGovern Medical School, Houston, TX., Tsao K; Division of General and Thoracic Pediatric Surgery, Department of Pediatric Surgery, UTHealth Houston McGovern Medical School, Houston, TX., Chang ML; From the Division of Infectious Diseases, Department of Pediatrics, UTHealth Houston McGovern Medical School, Houston, TX.
المصدر: Annals of surgery open : perspectives of surgical history, education, and clinical approaches [Ann Surg Open] 2022 May 23; Vol. 3 (2), pp. e168. Date of Electronic Publication: 2022 May 23 (Print Publication: 2022).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Wolters Kluwer Country of Publication: United States NLM ID: 101769928 Publication Model: eCollection Cited Medium: Internet ISSN: 2691-3593 (Electronic) Linking ISSN: 26913593 NLM ISO Abbreviation: Ann Surg Open Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Philadelphia, PA : Wolters Kluwer, [2020]-
مستخلص: Objective: To determine if artificial neural networks (ANN) could predict the risk of intra-abdominal abscess (IAA) development post-appendectomy.
Background: IAA formation occurs in 13.6% to 14.6% of appendicitis cases with "complicated" appendicitis as the most common cause of IAA. There remains inconsistency in describing the severity of appendicitis with variation in treatment with respect to perforated appendicitis.
Methods: Two "reproducible" ANN with different architectures were developed on demographic, clinical, and surgical information from a retrospective surgical dataset of 1574 patients less than 19 years old classified as either negative (n = 1,328) or positive (n = 246) for IAA post-appendectomy for appendicitis. Of 34 independent variables initially, 12 variables with the highest influence on the outcome selected for the final dataset for ANN model training and testing.
Results: A total of 1574 patients were used for training and test sets (80%/20% split). Model 1 achieved accuracy of 89.84%, sensitivity of 70%, and specificity of 93.61% on the test set. Model 2 achieved accuracy of 84.13%, sensitivity of 81.63%, and specificity of 84.6%.
Conclusions: ANN applied to selected variables can accurately predict patients who will have IAA post-appendectomy. Our reproducible and explainable ANNs potentially represent a state-of-the-art method for optimizing post-appendectomy care.
(Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)
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فهرسة مساهمة: Keywords: artificial intelligence; intraabdominal abscess; pediatric
تواريخ الأحداث: Date Created: 20230821 Latest Revision: 20230823
رمز التحديث: 20230823
مُعرف محوري في PubMed: PMC10431380
DOI: 10.1097/AS9.0000000000000168
PMID: 37601615
قاعدة البيانات: MEDLINE
الوصف
تدمد:2691-3593
DOI:10.1097/AS9.0000000000000168