Machine learning in chronic obstructive pulmonary disease

Zhang, Bochao; Wang, Jiping; Chen, Jing; Ling, Zongquan; Ren, Yuhao; Xiong, Daxi; Guo, Liquan

doi:10.1097/cm9.0000000000002247

Cited by 3 publications

(5 citation statements)

References 11 publications

(12 reference statements)

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“…Subsequently, ROC curve analysis was performed on the three characteristic bacteria enriched at the species level in the CS group ( Figure 6D ). The area under the curve (AUC) was calculated to determine the value of the operating characteristic curve in predicting disease ( Zhang et al, 2022 ). The results showed that L. johnsonii (AUC = 0.72) presented a large AUC value, indicating that L. johnsonii might be a potential biomarker for SSP in treating diarrhea with DYKS.…”

Section: Resultsmentioning

confidence: 99%

Sishen Pill inhibits intestinal inflammation in diarrhea mice via regulating kidney-intestinal bacteria-metabolic pathway

Li,

Qiao,

et al. 2024

Front. Pharmacol.

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BackgroundSishen Pill (SSP) has good efficacy in diarrhea with deficiency kidney-yang syndrome (DKYS), but the mechanism of efficacy involving intestinal microecology has not been elucidated.ObjectiveThis study investigated the mechanism of SSP in regulating intestinal microecology in diarrhea with DKYS.MethodsAdenine combined with Folium sennae was used to construct a mouse model of diarrhea with DKYS and administered with SSP. The behavioral changes and characteristics of gut content microbiota and short-chain fatty acids (SCFAs) of mice were analyzed to explore the potential association between the characteristic bacteria, SCFAs, intestinal inflammatory and kidney function-related indicators.ResultsAfter SSP intervention, the body weight and anal temperature of diarrhea with DKYS gradually recovered and approached the normal level. Lactobacillus johnsonii was significantly enriched, and propionic, butyric, isobutyric and isovaleric acids were elevated. Serum creatinine (Cr), interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α) levels of the mice were reduced, while serum blood urea nitrogen (BUN) and secretory immunoglobulin A (sIgA) in the colonic tissues were increased. Moreover, there were correlations between L. johnsonii, SCFAs, intestinal inflammatory, and kidney function.ConclusionSSP might suppress the intestinal inflammation by regulating the “L. johnsonii-propionic acid” pathway, thus achieving the effect of treating diarrhea with DKYS.

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Section: Resultsmentioning

confidence: 99%

Sishen Pill inhibits intestinal inflammation in diarrhea mice via regulating kidney-intestinal bacteria-metabolic pathway

Li,

Qiao,

et al. 2024

Front. Pharmacol.

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“…Consistent with the literature [31], the finding that the addition of basic age, gender, height, and weight variables boosts accuracy further confirms the value of the physiological context. Standardization of robust screening approaches can aid adoption [32].…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning with Impulse Oscillometry Data to Develop a Predictive Model for Chronic Obstructive Pulmonary Disease and Asthma

Huang,

Chou,

Perng

et al. 2024

JPM

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We aimed to develop and validate a machine learning model using impulse oscillometry system (IOS) profiles for accurately classifying patients into three assessment-based categories: no airflow obstruction, asthma, and chronic obstructive pulmonary disease (COPD). Our research questions were as follows: (1) Can machine learning methods accurately classify obstructive disease states based solely on multidimensional IOS data? (2) Which IOS parameters and modeling algorithms provide the best discrimination? We used data for 480 patients (240 with COPD and 240 with asthma) and 84 healthy individuals for training. Physiological and IOS parameters were combined into six feature combinations. The classification algorithms tested were logistic regression, random forest, neural network, k-nearest neighbor, and support vector machine. The optimal feature combination for identifying individuals without pulmonary obstruction, with asthma, or with COPD included 15 IOS and physiological features. The neural network classifier achieved the highest accuracy (0.786). For discriminating between healthy and unhealthy individuals, two combinations of twenty-three features performed best in the neural network algorithm (accuracy of 0.929). When distinguishing COPD from asthma, the best combination included 15 features and the neural network algorithm achieved an accuracy of 0.854. This study provides compelling technical evidence and clinical justifications for advancing IOS data-driven models to aid in COPD and asthma management.

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“…We used the R language to perform the entire data cleaning and modeling process. Drawing upon prior research, we utilized a diverse range of commonly employed machine learning techniques for feature modeling ( Alpaydin, 2014 ; Peng et al, 2020 ; Zhang et al, 2022a ), including neural networks ( Ripley, 2013 ), Support Vector Machine ( Karatzoglou et al, 2004 ) (SVM), Bayesian Generalized Linear Model ( Gelman & Hill, 2019 ), Random Forest ( Wright & Ziegler, 2017 ), C50 decision tree ( Kuhn, Weston & Coulter, 2018 ), k-nearest neighbor (KNN) ( Kuhn, 2008 ), AdaBoost ( Chatterjee, 2016 ), and xgboost ( Chen, He & Benesty, 2016 ). To ensure robust evaluation and control over model performance, we exclusively implemented five-fold cross-validation in the sub-training set ( Kuhn, 2008 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In recent years, machine learning (ML) models have emerged as a powerful tool for identifying disease risks, including chronic obstructive pulmonary disease (COPD) ( Ma et al, 2020 ; Peng et al, 2020 ; Zhang et al, 2022a ). Extensive research has demonstrated the accuracy of ML models in predicting the development of COPD based on genetic information and electronic medical records data ( Peng et al, 2020 ; Zhang et al, 2022a ). In particular, studies by Peng et al (2020) and Cosentino et al (2023) showcased the effectiveness of ML algorithms in detecting early signs of COPD from chest CT scans and in predicting the case-control status of COPD from raw high-dimensional spirograms, respectively ( Sugimori et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Predicting early-onset COPD risk in adults aged 20–50 using electronic health records and machine learning

Liu,

Hu,

Yang

et al. 2024

PeerJ

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Chronic obstructive pulmonary disease (COPD) is a major public health concern, affecting estimated 164 million people worldwide. Early detection and intervention strategies are essential to reduce the burden of COPD, but current screening approaches are limited in their ability to accurately predict risk. Machine learning (ML) models offer promise for improved accuracy of COPD risk prediction by combining genetic and electronic medical record data. In this study, we developed and evaluated eight ML models for primary screening of COPD utilizing routine screening data, polygenic risk scores (PRS), additional clinical data, or a combination of all three. To assess our models, we conducted a retrospective analysis of approximately 329,396 patients in the UK Biobank database. Incorporating personal information and blood biochemical test results significantly improved the model’s accuracy for predicting COPD risk, achieving a best performance of 0.8505 AUC, a specificity of 0.8539 and a sensitivity of 0.7584. These results indicate that ML models can be effectively utilized for accurate prediction of COPD risk in individuals aged 20 to 50 years, providing a valuable tool for early detection and intervention.

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Machine learning in chronic obstructive pulmonary disease

Cited by 3 publications

References 11 publications

Sishen Pill inhibits intestinal inflammation in diarrhea mice via regulating kidney-intestinal bacteria-metabolic pathway

Sishen Pill inhibits intestinal inflammation in diarrhea mice via regulating kidney-intestinal bacteria-metabolic pathway

Using Machine Learning with Impulse Oscillometry Data to Develop a Predictive Model for Chronic Obstructive Pulmonary Disease and Asthma

Predicting early-onset COPD risk in adults aged 20–50 using electronic health records and machine learning

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