Development of machine learning models for the surveillance of colon surgical site infections

Cho, SangYun; Kim, Z.; Chung, Doo Ryeon; Cho, B.H.; Chung, Myung Jin; Kim, J.H.; Jeong, Jae Sim

doi:10.1016/j.jhin.2023.03.025

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Surgical site infections (SSIs) are among the most common types of postoperative complications associated with substantial morbidity and mortality, prolonged hospital stay, and consequent financial burden to healthcare systems worldwide [ 140 ]. Expectedly, several groups have applied ML to create predictive models for SSIs [ 141 , 142 , 143 , 144 , 145 , 146 ]. In 2021, Petrosyan et al utilized heath administrative datasets to develop an efficient three-stage algorithm to identify SSIs within 30 days after surgery.…”

Section: Resultsmentioning

confidence: 99%

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Baddal,

Taner,

Uzun Ozsahin

2024

Diagnostics

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Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

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Section: Resultsmentioning

confidence: 99%

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Baddal,

Taner,

Uzun Ozsahin

2024

Diagnostics

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“…However, there has been no approach to study the NE moment prediction using artificial intelligence (AI) techniques (“Challenge 2”). Recently, AI technology has been broadly employed in various digital healthcare applications, such as clinical deterioration prediction [ 26 ], infection detection [ 27 ], clinical decision support systems [ 28 ], energy expenditure estimation [ 29 ], and medical twins in drug delivery applications [ 30 ]. Since AI excels in the capture and analysis of nonlinear and complex patterns from high-dimensional data, it has significantly contributed to developing novel methods for diagnosis, treatment, and prevention in digital healthcare.…”

Section: Introductionmentioning

confidence: 99%

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Lee,

Moon,

Lee

et al. 2024

Sensors

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Nocturnal enuresis (NE) is involuntary bedwetting during sleep, typically appearing in young children. Despite the potential benefits of the long-term home monitoring of NE patients for research and treatment enhancement, this area remains underexplored. To address this, we propose NEcare, an in-home monitoring system that utilizes wearable devices and machine learning techniques. NEcare collects sensor data from an electrocardiogram, body impedance (BI), a three-axis accelerometer, and a three-axis gyroscope to examine bladder volume (BV), heart rate (HR), and periodic limb movements in sleep (PLMS). Additionally, it analyzes the collected NE patient data and supports NE moment estimation using heuristic rules and deep learning techniques. To demonstrate the feasibility of in-home monitoring for NE patients using our wearable system, we used our datasets from 30 in-hospital patients and 4 in-home patients. The results show that NEcare captures expected trends associated with NE occurrences, including BV increase, HR increase, and PLMS appearance. In addition, we studied the machine learning-based NE moment estimation, which could help relieve the burdens of NE patients and their families. Finally, we address the limitations and outline future research directions for the development of wearable systems for NE patients

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Surgical site infections in the Far East

Morikane

2024

Journal of Hospital Infection

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Development of machine learning models for the surveillance of colon surgical site infections

Cited by 8 publications

References 23 publications

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Surgical site infections in the Far East

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