Selection of OSA-specific pronunciations and assessment of disease severity assisted by machine learning

Ding, Yiming; Sun, Yuechuan; Li, Yanru; Wang, Huijun; Fang, Qiang; Xu, Wen; Chen, Xiaohong; Wu, Ji; Gao, Jiandong; Han, Demin

doi:10.5664/jcsm.9798

Cited by 9 publications

(9 citation statements)

References 21 publications

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“…Machine learning is a subfield of artificial intelligence that allows algorithms to improve their performance on certain tasks based on empirical data (Handelman et al, 2018;Bi et al, 2019;Choi et al, 2020). In recent years, with the development of interdisciplinary, machine learning, as a research hotspot of artificial intelligence, has been widely used in the medical field (Connor, 2019;Sultan et al, 2020;Ding et al, 2021;Hornstein et al, 2021;Huang et al, 2021;Hung et al, 2021;Santos, 2021). In many cases, machine learning algorithms can better describe the complexity and unpredictability of human physiology (Heo et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Advancing Prediction of Risk of Intraoperative Massive Blood Transfusion in Liver Transplantation With Machine Learning Models. A Multicenter Retrospective Study

Chen

Liu

Wang

et al. 2022

Front. Neuroinform.

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BackgroundLiver transplantation surgery is often accompanied by massive blood loss and massive transfusion (MT), while MT can cause many serious complications related to high mortality. Therefore, there is an urgent need for a model that can predict the demand for MT to reduce the waste of blood resources and improve the prognosis of patients.ObjectiveTo develop a model for predicting intraoperative massive blood transfusion in liver transplantation surgery based on machine learning algorithms.MethodsA total of 1,239 patients who underwent liver transplantation surgery in three large grade lll-A general hospitals of China from March 2014 to November 2021 were included and analyzed. A total of 1193 cases were randomly divided into the training set (70%) and test set (30%), and 46 cases were prospectively collected as a validation set. The outcome of this study was an intraoperative massive blood transfusion. A total of 27 candidate risk factors were collected, and recursive feature elimination (RFE) was used to select key features based on the Categorical Boosting (CatBoost) model. A total of ten machine learning models were built, among which the three best performing models and the traditional logistic regression (LR) method were prospectively verified in the validation set. The Area Under the Receiver Operating Characteristic Curve (AUROC) was used for model performance evaluation. The Shapley additive explanation value was applied to explain the complex ensemble learning models.ResultsFifteen key variables were screened out, including age, weight, hemoglobin, platelets, white blood cells count, activated partial thromboplastin time, prothrombin time, thrombin time, direct bilirubin, aspartate aminotransferase, total protein, albumin, globulin, creatinine, urea. Among all algorithms, the predictive performance of the CatBoost model (AUROC: 0.810) was the best. In the prospective validation cohort, LR performed far less well than other algorithms.ConclusionA prediction model for massive blood transfusion in liver transplantation surgery was successfully established based on the CatBoost algorithm, and a certain degree of generalization verification is carried out in the validation set. The model may be superior to the traditional LR model and other algorithms, and it can more accurately predict the risk of massive blood transfusions and guide clinical decision-making.

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Section: Introductionmentioning

confidence: 99%

Advancing Prediction of Risk of Intraoperative Massive Blood Transfusion in Liver Transplantation With Machine Learning Models. A Multicenter Retrospective Study

Chen

Liu

Wang

et al. 2022

Front. Neuroinform.

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“…Untreated OSA is linked to an increased risk of cardiovascular issues, which increases the risk of severe COVID-19 infection and death in what appears to be a vicious cycle [ 39 ]. As a result, telemedicine for OSA management assistance and the use of portable screening equipment combined with artificial intelligence for prescreening suspected OSA might be advantageous [ [40] , [41] , [42] ]. According to our results, severe COVID-19 is causally related with OSA, shedding fresh light on the mechanisms underlying the relationship between OSA and COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Causal associations between obstructive sleep apnea and COVID-19: A bidirectional Mendelian randomization study

et al. 2023

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“…They classified the participants with thresholds of AHI =30 and AHI =10 events/hour by using linear prediction cepstral coefficients (LPCC) and SVM, which both obtained an accuracy of 78%. Recently, the researchers expanded on their investigation of the classification performance of Chinese syllables for OSA by using LPC and the decision tree model: specific Chinese syllables, such as [leng] and [jue], consonant pronunciations such as [zh] and [f], and vowel pronunciations such as [ing] and [ai], were found to be particularly effective for OSA classification ( 38 ). This study highlights the efficacy of using Chinese pronunciation as a reliable feature for predicting OSA and provides a comprehensive reference for the selection of an OSA corpus.…”

Section: Speech and Osamentioning

confidence: 99%

“…In general, speech appears to be a useful and an easily accessible predictor for OSA, which can optimize traditional OSA screening and diagnosis. Nevertheless, it should be noted that the studies by Ding et al ( 37 , 38 ) mainly focused on male patients, and thus it would be interesting to further clarify whether this method is suitable for testing the female population. Further research using different acoustic features, approaches, languages, or recording positions are expected to bring new insights into this field.…”

Section: Speech and Osamentioning

confidence: 99%

Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review

Cao,

Rosenzweig,

Bilotta

et al. 2024

J Thorac Dis

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Background and Objective Obstructive sleep apnea (OSA) is a common chronic disorder characterized by repeated breathing pauses during sleep caused by upper airway narrowing or collapse. The gold standard for OSA diagnosis is the polysomnography test, which is time consuming, expensive, and invasive. In recent years, more cost-effective approaches for OSA detection based in predictive value of speech and snoring has emerged. In this paper, we offer a comprehensive summary of current research progress on the applications of speech or snoring sounds for the automatic detection of OSA and discuss the key challenges that need to be overcome for future research into this novel approach. Methods PubMed, IEEE Xplore, and Web of Science databases were searched with related keywords. Literature published between 1989 and 2022 examining the potential of using speech or snoring sounds for automated OSA detection was reviewed. Key Content and Findings Speech and snoring sounds contain a large amount of information about OSA, and they have been extensively studied in the automatic screening of OSA. By importing features extracted from speech and snoring sounds into artificial intelligence models, clinicians can automatically screen for OSA. Features such as formant, linear prediction cepstral coefficients, mel-frequency cepstral coefficients, and artificial intelligence algorithms including support vector machines, Gaussian mixture model, and hidden Markov models have been extensively studied for the detection of OSA. Conclusions Due to the significant advantages of noninvasive, low-cost, and contactless data collection, an automatic approach based on speech or snoring sounds seems to be a promising tool for the detection of OSA.

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Selection of OSA-specific pronunciations and assessment of disease severity assisted by machine learning

Cited by 9 publications

References 21 publications

Advancing Prediction of Risk of Intraoperative Massive Blood Transfusion in Liver Transplantation With Machine Learning Models. A Multicenter Retrospective Study

Advancing Prediction of Risk of Intraoperative Massive Blood Transfusion in Liver Transplantation With Machine Learning Models. A Multicenter Retrospective Study

Causal associations between obstructive sleep apnea and COVID-19: A bidirectional Mendelian randomization study

Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review

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