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An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning.

ABSTRACT: BACKGROUND:Diagnostic evaluation of eosinophilic esophagitis (EoE) remains difficult, particularly the assessment of the patient's allergic status. OBJECTIVE:This study sought to establish an automated medical algorithm to assist in the evaluation of EoE. METHODS:Machine learning techniques were used to establish a diagnostic probability score for EoE, p(EoE), based on esophageal mRNA transcript patterns from biopsies of patients with EoE, gastroesophageal reflux disease and controls. Dimensionality reduction in the training set established weighted factors, which were confirmed by immunohistochemistry. Following weighted factor analysis, p(EoE) was determined by random forest classification. Accuracy was tested in an external test set, and predictive power was assessed with equivocal patients. Esophageal IgE production was quantified with epsilon germ line (IGHE) transcripts and correlated with serum IgE and the Th2-type mRNA profile to establish an IGHE score for tissue allergy. RESULTS:In the primary analysis, a 3-class statistical model generated a p(EoE) score based on common characteristics of the inflammatory EoE profile. A p(EoE) ? 25 successfully identified EoE with high accuracy (sensitivity: 90.9%, specificity: 93.2%, area under the curve: 0.985) and improved diagnosis of equivocal cases by 84.6%. The p(EoE) changed in response to therapy. A secondary analysis loop in EoE patients defined an IGHE score of ?37.5 for a patient subpopulation with increased esophageal allergic inflammation. CONCLUSIONS:The development of intelligent data analysis from a machine learning perspective provides exciting opportunities to improve diagnostic precision and improve patient care in EoE. The p(EoE) and the IGHE score are steps toward the development of decision trees to define EoE subpopulations and, consequently, will facilitate individualized therapy.

SUBMITTER: Sallis BF

PROVIDER: S-EPMC6425755 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning.

Sallis Benjamin F BF Erkert Lena L Moñino-Romero Sherezade S Acar Utkucan U Wu Rina R Konnikova Liza L Lexmond Willem S WS Hamilton Matthew J MJ Dunn W Augustine WA Szepfalusi Zsolt Z Vanderhoof Jon A JA Snapper Scott B SB Turner Jerrold R JR Goldsmith Jeffrey D JD Spencer Lisa A LA Nurko Samuel S Fiebiger Edda E

The Journal of allergy and clinical immunology 20171219 4

<h4>Background</h4>Diagnostic evaluation of eosinophilic esophagitis (EoE) remains difficult, particularly the assessment of the patient's allergic status.<h4>Objective</h4>This study sought to establish an automated medical algorithm to assist in the evaluation of EoE.<h4>Methods</h4>Machine learning techniques were used to establish a diagnostic probability score for EoE, p(EoE), based on esophageal mRNA transcript patterns from biopsies of patients with EoE, gastroesophageal reflux disease an ...[more]

PMID: 29273402

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Project description:BackgroundPrognosis, recurrence rate, and secondary prevention strategies differ by different etiologies in acute ischemic stroke. However, identifying its cause is challenging.ObjectiveThis study aimed to develop a model to identify the cause of stroke using machine learning (ML) methods and test its accuracy.MethodsWe retrospectively reviewed the data of patients who had determined etiology defined by the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) from CASE-II (NCT04487340) to train and evaluate six ML models, namely, Random Forests (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), Ada Boosting, Gradient Boosting Machine (GBM), for the detection of cardioembolism (CE), large-artery atherosclerosis (LAA), and small-artery occlusion (SAO). Between October 2016 and April 2020, patients were enrolled consecutively for algorithm development (phase one). Between June 2020 and December 2020, patients were enrolled consecutively in a test set for algorithm test (phase two). Area under the curve (AUC), precision, recall, accuracy, and F1 score were calculated for the prediction model.ResultsFinally, a total of 18,209 patients were enrolled in phase one, including 13,590 patients (i.e., 6,089 CE, 4,539 LAA, and 2,962 SAO) in the model, and a total of 3,688 patients were enrolled in phase two, including 3,070 patients (i.e., 1,103 CE, 1,269 LAA, and 698 SAO) in the model. Among the six models, the best models were RF, XGBoost, and GBM, and we chose the RF model as our final model. Based on the test set, the AUC values of the RF model to predict CE, LAA, and SAO were 0.981 (95%CI, 0.978-0.986), 0.919 (95%CI, 0.911-0.928), and 0.918 (95%CI, 0.908-0.927), respectively. The most important items to identify CE, LAA, and SAO were atrial fibrillation and degree of stenosis of intracranial arteries.ConclusionThe proposed RF model could be a useful diagnostic tool to help neurologists categorize etiologies of stroke.Clinical trial registration[www.ClinicalTrials.gov], identifier [NCT01274117].

Dataset Information

An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning.

Publications

An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning.

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