Emergent Applications of Machine Learning for Diagnosing and Managing Appendicitis: A State-of-the-Art Review-Reference-Cited by-同舟云学术

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Emergent Applications of Machine Learning for Diagnosing and Managing Appendicitis: A State-of-the-Art Review

Published:2024-02-01 Issue:1 Volume:25 Page:7-18
ISSN:1096-2964
Container-title:Surgical Infections
language:en
Short-container-title:Surgical Infections

Author:

Bhandarkar Shaan¹,Tsutsumi Ayaka²,Schneider Eric B.²,Ong Chin Siang²,Paredes Lucero²,Brackett Alexandria³,Ahuja Vanita²

Affiliation:

1. Yale University, New Haven, Connecticut, USA.

2. Department of Surgery, Yale School of Medicine, Yale School of Medicine, New Haven, Connecticut, USA.

3. Harvey Cushing/John Hay Whitney Medical Library, Yale School of Medicine, New Haven, Connecticut, USA.

Publisher

Mary Ann Liebert Inc

Subject

Infectious Diseases,Microbiology (medical),Surgery

Link

https://www.liebertpub.com/doi/pdf/10.1089/sur.2023.201

Reference55 articles.

1. Early operative management of complicated appendicitis is associated with improved surgical outcomes in adults

2. Factors Associated With Potentially Missed Diagnosis of Appendicitis in the Emergency Department

3. Negative Appendicectomy Rate: Incidence and Predictors

4. The Alvarado score for predicting acute appendicitis: a systematic review

5. Optimizing integrated imaging service delivery by tier in low-resource health systems

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Applying an explainable machine learning model might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis;Scientific Reports;2024-06-04

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