Using machine learning analysis to assist in differentiating between necrotizing enterocolitis and spontaneous intestinal perforation: A novel predictive analytic tool

Lure, Allison C.; Du, Xinsong; Black, Erik W.; Irons, Raechel; Lemas, Dominick J.; Taylor, Janice A.; Lavilla, Orlyn C.; Cruz, Diomel de la; Neu, Josef

doi:10.1016/j.jpedsurg.2020.11.008

Cited by 33 publications

(12 citation statements)

References 31 publications

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“…Supervised machine learning was used to evaluate NEC versus spontaneous intestinal perforation (SIP), which can be differentiated at laparotomy, but the increasing use of peritoneal drains without direct visualization of the bowel limits the accuracy of the diagnosis. In a recent study that evaluated several clinical and radiographic features in infants who had undergone surgery where necrosis was clearly found versus isolated ileal perforation, machine learning was able to readily delineate several features that are helpful in differentiating these two outcomes prior to surgery 47 .…”

Section: Clinical Applicationsmentioning

confidence: 99%

Multiomics, artificial intelligence, and precision medicine in perinatology

2022

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Technological advances in omics evaluation, bioinformatics and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multi-omics datasets by machine learning promises us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical datasets in the field of Perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical datasets will make precision medicine a reality.

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Section: Clinical Applicationsmentioning

confidence: 99%

Multiomics, artificial intelligence, and precision medicine in perinatology

2022

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“…As an example, let n = 20 and filp = 5, where n denotes the number of features in Refsol. If the index of features are 0 to 19, feature subsets f 0 , f 1 , f 2 , f 3 and f 4 are obtained by flipping the following bits, as shown in Fig 4: (0,5,10,15), (1,6,11,16), (2,7,12,17), (3,8,13,18) and (4,9,14,19). In the second strategy, the k-th subset of features is obtained by flipping n/flip contiguous bits starting from the k-th bit.…”

Section: Plos Onementioning

confidence: 99%

“…In the second strategy, the k-th subset of features is obtained by flipping n/flip contiguous bits starting from the k-th bit. Following the previous example, the feature subsets f 0 , f 1 , f 2 , f 3 and f 4 are obtained by flipping the following bits: (0,1,2,3), (4,5,6,7), (8,9,10,11), (12,13,14,15) and (16,17,18,19). With the above two searching strategies, we determine the search area for each bee.…”

Section: Plos Onementioning

confidence: 99%

“…In the feature selection stage, they use statistical analysis to extract important features. In the classification stage, most researchers use ML methods such as linear discriminant analysis (LDA) [14,15], random forest (RF) [16][17][18], or Light Gradient Boosting Machine (GBM) [19] as classifier models. Table 1 summarizes some studies using ML for the diagnosis or prognosis of NEC.…”

Section: Introductionmentioning

confidence: 99%

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Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis

Song

Yao

et al. 2022

PLoS ONE

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Neonatal necrotizing enterocolitis (NEC) occurs worldwide and is a major source of neonatal morbidity and mortality. Researchers have developed many methods for predicting NEC diagnosis and prognosis. However, most people use statistical methods to select features, which may ignore the correlation between features. In addition, because they consider a small dimension of characteristics, they neglect some laboratory parameters such as white blood cell count, lymphocyte percentage, and mean platelet volume, which could be potentially influential factors affecting the diagnosis and prognosis of NEC. To address these issues, we include more perinatal, clinical, and laboratory information, including anemia—red blood cell transfusion and feeding strategies, and propose a ridge regression and Q-learning strategy based bee swarm optimization (RQBSO) metaheuristic algorithm for predicting NEC diagnosis and prognosis. Finally, a linear support vector machine (linear SVM), which specializes in classifying high-dimensional features, is used as a classifier. In the NEC diagnostic prediction experiment, the area under the receiver operating characteristic curve (AUROC) of dataset 1 (feeding intolerance + NEC) reaches 94.23%. In the NEC prognostic prediction experiment, the AUROC of dataset 2 (medical NEC + surgical NEC) reaches 91.88%. Additionally, the classification accuracy of the RQBSO algorithm on the NEC dataset is higher than the other feature selection algorithms. Thus, the proposed approach has the potential to identify predictors that contribute to the diagnosis of NEC and stratification of disease severity in a clinical setting.

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“…To date, only a few studies have used ML algorithms to predict IP in premature infants. Irles et al 31 reported an ML model predicting NEC-IP, and Lure et al 32 reported an ML model to differentiate NEC-IP and SIP prior to surgical interventions. Lin 33 et al reported an ML technique for individualized NEC risk scores using intestinal microbiota data.…”

Section: Introductionmentioning

confidence: 99%

Development of artificial neural networks for early prediction of intestinal perforation in preterm infants

Son

Kim

et al. 2022

Sci Rep

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Intestinal perforation (IP) in preterm infants is a life-threatening condition that may result in serious complications and increased mortality. Early Prediction of IP in infants is important, but challenging due to its multifactorial and complex nature of the disease. Thus, there are no reliable tools to predict IP in infants. In this study, we developed new machine learning (ML) models for predicting IP in very low birth weight (VLBW) infants and compared their performance to that of classic ML methods. We developed artificial neural networks (ANNs) using VLBW infant data from a nationwide cohort and prospective web-based registry. The new ANN models, which outperformed all other classic ML methods, showed an area under the receiver operating characteristic curve (AUROC) of 0.8832 for predicting IP associated with necrotizing enterocolitis (NEC-IP) and 0.8797 for spontaneous IP (SIP). We tested these algorithms using patient data from our institution, which were not included in the training dataset, and obtained an AUROC of 1.0000 for NEC-IP and 0.9364 for SIP. NEC-IP and SIP in VLBW infants can be predicted at an excellent performance level with these newly developed ML models. https://github.com/kdhRick2222/Early-Prediction-of-Intestinal-Perforation-in-Preterm-Infants.

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Using machine learning analysis to assist in differentiating between necrotizing enterocolitis and spontaneous intestinal perforation: A novel predictive analytic tool

Cited by 33 publications

References 31 publications

Multiomics, artificial intelligence, and precision medicine in perinatology

Multiomics, artificial intelligence, and precision medicine in perinatology

Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis

Development of artificial neural networks for early prediction of intestinal perforation in preterm infants

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