Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study

Song, Chi; Li, Li; Zhang, Yongjiang; Lu, Jianwei; Wang, Xiuzhen; Song, Xiaoyan; Liu, Jinghua; Li, Kefeng

doi:10.1002/advs.202003893

Cited by 19 publications

(14 citation statements)

References 46 publications

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“…Machine learning approaches can process high dimensional variables and assess all the brain compartments simultaneously [ 84 ]. A recent study developed a classification model using SVM and structural MRI data to identify suicide attempters in adolescent MDD patients with an accuracy of 78.6% and sensitivity of 73.2% [ 85 ].…”

Section: Brain Functional Connectome For Predicting Suicide Risks In Mdd Patientsmentioning

confidence: 99%

Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome

et al. 2021

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Brain function relies on efficient communications between distinct brain systems. The pathology of major depressive disorder (MDD) damages functional brain networks, resulting in cognitive impairment. Here, we reviewed the associations between brain functional connectome changes and MDD pathogenesis. We also highlighted the utility of brain functional connectome for differentiating MDD from other similar psychiatric disorders, predicting recurrence and suicide attempts in MDD, and evaluating treatment responses. Converging evidence has now linked aberrant brain functional network organization in MDD to the dysregulation of neurotransmitter signaling and neuroplasticity, providing insights into the neurobiological mechanisms of the disease and antidepressant efficacy. Widespread connectome dysfunctions in MDD patients include multiple, large-scale brain networks as well as local disturbances in brain circuits associated with negative and positive valence systems and cognitive functions. Although the clinical utility of the brain functional connectome remains to be realized, recent findings provide further promise that research in this area may lead to improved diagnosis, treatments, and clinical outcomes of MDD.

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Section: Brain Functional Connectome For Predicting Suicide Risks In Mdd Patientsmentioning

confidence: 99%

Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome

et al. 2021

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“…1A ). Random forest (RF) is a widely used machine learning algorithm for the construction of predictive models due to its resilience to high dimensionality, insensitivity to noise, and robustness to overfitting ( 19 , 20 ). We applied RF to identify which variables have more determinant impact on the prediction outcomes.…”

Section: Resultsmentioning

confidence: 99%

Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy

Duan

Zhang

et al. 2022

Endocrine Connections

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Background and objective: Radioiodine therapy (RAI) is one of the most common treatment solutions for Graves' disease (GD). However, many patients will develop hypothyroidism as early as 6 months after RAI. This study aimed to implement machine learning (ML) algorithms for early prediction of post-RAI hypothyroidism. Methods: 471 GD patients who underwent RAI between January 2016 and June 2019 were retrospectively recruited and randomly split into the training set (310 patients) and the validation set (161 patients). These patients were followed for 6 months after RAI. A set of 138 clinical and lab test features from the electronic medical record (EMR) were extracted, and multiple ML algorithms were conducted to identify the features associated with the occurrence of hypothyroidism at 6 months after RAI. Results: An integrated multivariate model containing patients’ age, thyroid mass, 24 h radioactive iodine uptake, serum concentrations of aspartate aminotransferase (AST), thyrotropin-receptor antibodies (TRAb), thyroid microsomal antibodies (TMA), and blood neutrophil count demonstrated an area under the ROC (AUROC) of 0.72 (95% CI: 0.61 - 0.85), an F1 score of 0.74, and an MCC score of 0.63 in the training set. The model also performed well in the validation set with an AUROC of 0.74 (95% CI: 0.65 - 0.83), an F1 score of 0.74, and a MCC of 0.63. A user-friendly nomogram was then established to facilitate the clinical utility. Conclusion: The developed multivariate model based on EMR data could be a valuable tool for predicting post-RAI hypothyroidism, allowing them to be treated differently before the therapy. Further study is needed to validate the developed prognostic model at independent sites.

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“…LC-MS is used to compare the metabolic profiles of different treatment groups, which reveals the differential metabolites between groups and helps clarify the mechanisms of disease [32,33]. At present, LC-MS-based metabolomics is widely used in disease diagnosis, drug development, treatment efficacy evaluation, and outcome prediction [28,34,35].…”

Section: Liquid Chromatography-mass Spectrometrymentioning

confidence: 99%

Recent application of metabolomics in the diagnosis, pathogenesis, treatment, and prognosis of sepsis

2021

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Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infections. It is a leading cause of morbidity and mortality in hospitalized patients. Patients with sepsis often require care in the intensive care unit (ICU) which is costly to the patients and their families. Sepsis has no specific clinical manifestations, and its pathophysiological mechanism is complex. The disease progresses rapidly which makes early diagnosis difficult. Severe forms of the disease, such as septic shock, may lead to organ dysfunction, organ failure, and death. As an emerging “-omics” technology, metabolomics has revolutionized the clinical and research landscape of sepsis. Metabolomics has been applied in the prognosis, diagnosis, and risk stratification in patients with sepsis. This technology provides details on the metabolites and biochemical pathways commonly associated with the pathophysiology of sepsis. At present, it is mostly used to identify metabolites in various diseases. Using this technology, metabolites in body fluids such as blood and urine are detected and analyzed in relation to disease progresssion. The technology therefore helps to understand the pathogenesis of diseases and promote early diagnosis and treatment of the disease. So far, the applicaition of metabolomics in patients with sepsis has not been well defined. This article briefly reviews the application of metabolomics technology in patients with sepsis in recent years, to generate ideas for improving rapid diagnosis and prognosis evaluation of patients with sepsis.

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Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study

Cited by 19 publications

References 46 publications

Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome

Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome

Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy

Recent application of metabolomics in the diagnosis, pathogenesis, treatment, and prognosis of sepsis

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