Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer

Kuo, Pao‐Jen; Wu, Shao-Chun; Chien, Peng‐Chen; Chang, Shu-Shya; Rau, Cheng-Shyuan; Tai, Hsueh-Ling; Peng, Shu-Hui; Lin, Yi-Chien; Chen, Yi‐Chun; Hsieh, Hsiao-Yun; Hsieh, Ching‐Hua

doi:10.18632/oncotarget.24468

Cited by 48 publications

(38 citation statements)

References 57 publications

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“…Among the five ML-CDSS to diagnose bacterial infection in hospitalized patients, two ML-CDSS included any kind of infection [27,51], two focused on the prediction of positive blood cultures [53,54] and one on MRSA infection [52]. The ML-CDSS for patients hospitalized in surgical wards aimed to diagnose SSIs following open abdominal surgery [55,57] surgery for head or neck cancer [56] or following any intervention on neonates [30].…”

Section: Diagnosis Of Infectionmentioning

confidence: 99%

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

Peiffer-Smadja

Rawson

Ahmad

et al. 2020

Clinical Microbiology and Infection

319

174

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Background: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). Objectives: We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID. Sources: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019. Content: We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n ¼ 24, 40%), ID consultation (n ¼ 15, 25%), medical or surgical wards (n ¼ 13, 20%), emergency department (n ¼ 4, 7%), primary care (n ¼ 3, 5%) and antimicrobial stewardship (n ¼ 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low-and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%). Implications: Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

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Section: Diagnosis Of Infectionmentioning

confidence: 99%

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

Peiffer-Smadja

Rawson

Ahmad

et al. 2020

Clinical Microbiology and Infection

319

174

View full text Add to dashboard Cite

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“…The China Medical University Hospital (Taichung, Taiwan) is a 2000-bed facility and a level I trauma center that provides 24 h on duty team of trauma surgeon, orthopedic surgeon, and interventional radiologist to trauma patients; it serves a population of approximately 3 million residents in central Taiwan [17, 18]. Annually, approximately 2500 trauma patients and 600 major trauma patients with an Injury Severity Score (ISS) ≥ 16 are hospitalized through the emergency department (ED).…”

Section: Methodsmentioning

confidence: 99%

Trends and predictors of mortality in unstable pelvic ring fracture: a 10-year experience with a multidisciplinary institutional protocol

Chen

Hsieh

et al. 2019

World J Emerg Surg

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BackgroundPelvic ring fracture is often combined with other injuries and such patients are considered at high risk of mortality and complications. There is controversy regarding the gold standard protocol for the initial treatment of pelvic fracture. The aim of this study was to assess which risk factors could affect the outcome and to analyze survival using our multidisciplinary institutional protocol for traumatic pelvic ring fracture.Material and methodsThis retrospective study reviewed patients who sustained an unstable pelvic ring fracture with Injury Severity Score (ISS) ≥ 5. All patients were admitted to the emergency department and registered in the Trauma Registry System of a level I trauma center from January 1, 2008, to December 31, 2017. The annular mortality rate after the application of our institutional protocol was analyzed. Patients with different systems of injury and treatments were compared, and regression analysis was performed to adjust for factors that could affect the rate of mortality and complications.ResultsDuring the 10-year study period, there were 825 unstable pelvic ring injuries, with a mean ISS higher than that of other non-pelvic trauma cases. The annual mortality rate declined from 7.8 to 2.4% and the mean length of stay was 18.1 days. A multivariable analysis showed that unstable initial vital signs, such as systolic blood pressure < 90 mmHg (odds ratio [OR] 2.53; confidence interval [CI] 1.11–5.73), Glasgow Coma Scale < 9 (OR 3.87; CI 1.57–9.58), 24 > ISS > 15 (OR 4.84; CI 0.85–27.65), pulse rate < 50 (OR 11.54; CI 1.21–109.6), and diabetes mellitus (OR 3.18; CI 1.10–9.21) were associated with higher mortality. No other specific system in the high Abbreviated Injury Scale increased the rates of mortality or complications.ConclusionPoor initial vital signs and Glasgow Coma Scale score, higher ISS score, and comorbidity of diabetes mellitus affect the mortality rate of patients with unstable pelvic ring fractures. No single system of injury was found to increase mortality in these patients. The mortality rate was reduced through institutional efforts toward the application of guidelines for the initial management of pelvic fracture.

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“…Whenever a diagnosis is solely reliant upon a visual stimulus, for example 2D photography or CT, ML has consistently and reliably outperformed surgeons’ diagnostic accuracy. 18 , 37 , 39 , 40 , 46 , 51 , 53 , 59 , 63 Further, in conditions in which there are well-established correlations between certain risk markers and an outcome of interest, such as deranged blood tests on admission and AKI in burn patients, ML yielded highly accurate predictive algorithms. 38 , 44 , 55 24 47 However, attempts to include weakly related risk markers resulted in algorithms that had an overall lower predictive accuracy, rendering them unsafe for clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

Mantelakis

Sorooshian

Khajuria

2021

Plastic and Reconstructive Surgery - Global Open

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Introduction: Machine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research. Methods: EMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation. Results: The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9). Conclusions: ML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.

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Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer

Cited by 48 publications

References 57 publications

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

Trends and predictors of mortality in unstable pelvic ring fracture: a 10-year experience with a multidisciplinary institutional protocol

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

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