Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng, Yinhe; Wang, Yubin; Zeng, Chunfang; Mao, Hui

doi:10.7150/ijms.58191

Cited by 50 publications

(21 citation statements)

References 114 publications

(210 reference statements)

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“…This difference can be easily explained by the content of the databases used: our purpose was to develop an algorithm applicable to databases with no clinical data or diagnostic label. Conversely, studies that found better performance used more extensive data such as: results of spirometry, smoking status, physical examination and imaging [ 6 , 33 , 34 ]. For instance, Spathis and Valmos found 97.7 per cent diagnostic precision using random forest in COPD, relying on multiple elements such as smoking, age and spirometric data (FEV1 and FVC) [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

“…Nowadays, artificial intelligence tools and computer based methods are on the rise and are gradually improving the quality of care by supporting physicians for the diagnosis as well as for the management and follow up [4][5][6]. Many types of algorithms have been used since the 1990s, such as artificial neural networks (ANNs), fuzzy logic (FL), Random Forests, Gradient Boosting and Logistic Regression.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence to differentiate asthma from COPD in medico-administrative databases

et al. 2022

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Introduction Discriminating asthma from chronic obstructive pulmonary disease (COPD) using medico-administrative databases is challenging but necessary for medico-economic analyses focusing on respiratory diseases. Artificial intelligence (AI) may improve dedicated algorithms. Objectives To assess performance of different AI-based approaches to distinguish asthmatics from COPD patients in medico-administrative databases where the clinical diagnosis is absent. An “Asthma COPD Overlap” category was defined to further test whether AI can detect complexity. Methods This study included 178,962 patients treated by two “R03” treatment prescriptions at least from January 2016 to December 2018 and managed by either a general practitioner and/or a pulmonologist participating in a permanent longitudinal observatory of prescription in ambulatory medicine (LPD). Clinical diagnoses are available in this database and were used as gold standards to develop diagnostic rules. Three types of AI approaches were explored using data restricted to demographics and treatment dispensations: multinomial regression, gradient boosting and recurrent neural networks (RNN). The best performing model (based on metric properties) was then applied to estimate the size of asthma and COPD populations based on a database (LRx) of treatment dispensations between July, 2018 and June, 2019. Results The best models were obtained with the boosting approach and RNN, with an overall accuracy of 68%. Performance metrics were better for asthma than COPD. Based on LRx data, the extrapolated numbers of patients treated for asthma and COPD in France were 3.7 and 1.2 million, respectively. Asthma patients were younger than COPD patients (mean, 49.9 vs. 72.1 years); COPD occurred mostly in men (68%) compared to asthma (33%). Conclusion AI can provide models with acceptable accuracy to distinguish between asthma, ACO and COPD in medico-administrative databases where the clinical diagnosis is absent. Deep learning and machine learning (RNN) had similar performances in this regard.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence to differentiate asthma from COPD in medico-administrative databases

et al. 2022

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“…Recently, deep learning methods have dramatically improved different fields of medical care and research [21,22]. They have also been used as the core methods to build the CDSS [23,24]. For example, convolutional neural networks (CNNs) are used to process image data and recurrent neural networks (RNNs) are used for sequential pattern problems [23,25].…”

Section: Ivyspringmentioning

confidence: 99%

A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center

et al. 2022

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Background: Diabetes mellitus (DM) is a major public health problem worldwide. It involves dysfunction of blood sugar regulation resulting from insulin resistance, inadequate insulin secretion, or excessive glucagon secretion. Methods: This study collated 971,401 drug usage records of 51,009 DM patients. These data include patient identification code, age, gender, outpatient visiting dates, visiting code, medication features (included items, doses, and frequencies of drugs), HbA1c results, and testing time. We apply a random forest (RF) model for feature selection and implement a regression model with the bidirectional long short-term memory (Bi-LSTM) deep learning architecture. Finally, we use the root mean square error (RMSE) as the evaluation index for the prediction model. Results: After data cleaning, the data included 8,729 male and 9,115 female cases. Metformin was the most important feature suggested by the RF model, followed by glimepiride, acarbose, pioglitazone, glibenclamide, gliclazide, repaglinide, nateglinide, sitagliptin, and vildagliptin. The model performed better with the past two seasons in the training data than with additional seasons. Further, the Bi-LSTM architecture model performed better than support vector machines (SVMs). Discussion & Conclusion:This study found that Bi-LSTM models is a well kernel in a CDSS which help physicians' decision-making, and the increasing the number of seasons will negative impact the performance. In addition, this study found that the most important drug is metformin, which is recommended as first-line treatment OHA in various situations for DM patients.

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“…Over the bronchi, basis pulmonis, and lung periphery, normal breath sounds such as bronchial (BB), vesicular, and bronchovesicular are auscultated. Rhonchi (RB), crackle, wheeze, stridor, and pleural rub are examples of adventitious breath sounds that identify acquired lung diseases such as restriction in large airways, fluid accumulation in or around lungs, air passage constriction leading to inflation of a portion of the lungs, pleural layer inflammation, and others (2) . The pitch of the breath sound and the ratio of inspiration to expiration duration vary depending on where it originates.…”

Section: Introductionmentioning

confidence: 99%

Fractal and Time-series A nalyses based Rhonchi and Bronchial Auscultation: A Machine Learning Approach

Renjini¹,

Swapna²,

Raj³

et al. 2022

IJST

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Objectives:The present work reports the study of 34 rhonchi (RB) and Bronchial Breath (BB) signals employing machine learning techniques, timefrequency, fractal, and non-linear time-series analyses. Methods: The timefrequency analyses and the complexity in the dynamics of airflow in BB and RB are studied using both Power Spectral Density (PSD) features and non-linear measures. For accurate prediction of these signals, PSD and nonlinear measures are fed as input attributes to various machine learning models. Findings: The spectral analyses reveal fewer, low-intensity frequency components along with its overtones in the intermittent and rapidly damping RB signal. The complexity in the dynamics of airflow in BB and RB is investigated through the fractal dimension, Hurst exponent, phase portrait, maximal Lyapunov exponent, and sample entropy values. The greater value of entropy for the RB signal provides an insight into the internal morphology of the airways containing mucous and other obstructions. The Principal Component Analysis (PCA) employs PSD features, and Linear Discriminant Analysis (LDA) along with Pattern Recognition Neural Network (PRNN) uses non-linear measures for predicting BB and RB. Signal classification based on phase portrait features evaluates the multidimensional aspects of signal intensities, whereas that based on PSD features considers mere signal intensities. The principal components in PCA cover about 86.5% of the overall variance of the data class, successfully distinguishing BB and RB signals. LDA and PRNN that use nonlinear time-series parameters identify and predict RB and BB signals with 100% accuracy, sensitivity, specificity, and precision. Novelty: The study divulges the potential of non-linear measures and PSD features in classifying these signals enabling its application to be extended for low-cost, non-invasive COVID-19 detection and real-time health monitoring.

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Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Cited by 50 publications

References 114 publications

Artificial intelligence to differentiate asthma from COPD in medico-administrative databases

Artificial intelligence to differentiate asthma from COPD in medico-administrative databases

A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center

Fractal and Time-series A nalyses based Rhonchi and Bronchial Auscultation: A Machine Learning Approach

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