A prediction model based on an artificial intelligence system for moderate to severe obstructive sleep apnea

Sun, Lei Ming; Chiu, Hung Wen; Chuang, Chih Yuan; Liu, Li

doi:10.1007/s11325-010-0384-x

Cited by 40 publications

(23 citation statements)

References 15 publications

(15 reference statements)

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“…Rodsutti et al [35] conducted a study to develop and validate a decision rule (based on risk factors) that would allow prioritization on the waiting list, using univariate analysis and multiple logistic regression, achieving a scoring scheme or color-coded tables for easy clinical application. Sun et al [40] used three questionnaires to improve sensitivity and specificity for discrimination of moderate to severe OSA, based on a genetic algorithm. Montoya et al [37] based their work on several epidemiological and clinical variables, sought to find alternatives to PSG, using logistic regression analysis and multivariate logistic regression to determine the best model for distinguishing OSA patients from the healthy ones.…”

Section: Introductionmentioning

confidence: 99%

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Ferreira-Santos

Rodrigues

2018

Int J Data Sci Anal

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In obstructive sleep apnea, respiratory effort is maintained but ventilation decreases/disappears due to upper-airway partial/total occlusion. This condition affects about 4% of men and 2% of women worldwide. This study aimed to define an auxiliary diagnostic method that can support the decision to perform polysomnography, based on risk and diagnostic factors. Our sample performed polysomnography between January and May 2015. Two Bayesian classifiers were used to build the models: Naïve Bayes and Tree Augmented Naïve Bayes, using 38 variables identified by literature review or just a selection of 6. Area under the ROC curve, sensitivity, specificity and predictive values were evaluated using leave-one-out and cross-validation techniques. From a total of 241 patients, only 194 fulfilled the inclusion criteria, 123 (63%) were male, with a mean age of 58 years, 66 (34%) patients had a normal result and 128 (66%) a diagnosis of obstructive sleep apnea. The cross-validated AUCs for each model were: NB38: 69.2%; TAN38: 69.0%; NB6: 74.6% and TAN6: 63.6%. Regarding risk matrix, female gender presented a starting rate of 8%, comparing to 20% in male gender, almost 3 times higher. The high (34%) proportion of normal results confirms the need for a pre-evaluation prior to polysomnography, making the search for a validated model to screen patients with suspicion of obstructive sleep apnea essential, especially at primary care level.

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Section: Introductionmentioning

confidence: 99%

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Ferreira-Santos

Rodrigues

2018

Int J Data Sci Anal

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“…However, the complexity of the data and current requirements regarding their storage and accessibility necessitate the use of computers and sophisticated software. In the field of medical research, this has led to the implementation of bioinformatics [3] which is well suited to medical diagnostics, as demonstrated by the study described in this report.…”

Section: Introductionmentioning

confidence: 94%

A prediction model based on artificial neural networks for the diagnosis of obstructive sleep apnea

Karamanli

Yalçınöz

Yalcinoz³

et al. 2015

Sleep Breath

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“…Previous studies have used classi cation methods such as genetic algorithm (26) and support vector machine (27). In this paper, we used RF, which is an ensemble learning model that can be used to perform classi cation (28).…”

Section: Random Forestsmentioning

confidence: 99%

Risk Screening of Obstructive Sleep Apnea Syndrome by Body Profiles via Random Forests Model

Tsai

Lin

et al. 2020

Preprint

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Background Obstructive Sleep Apnea Syndrome (OSAS) is a major global health concern and is typically diagnosed by in-lab polysomnography (PSG). This examination though has high medical manpower costs and alternative portable methods have further limitations. This paper develops a new model for screening the risk of OSAS in different age groups and gender by using body profiles. The effects of body profiles for different subgroups in sleep stage alteration and OSAS severity are also investigated. Methods The data is derived from 6614 Han-Taiwanese subjects who have previously undergone PSG in order to assess the severity of OSAS in the sleep center of Taipei Medical University Shuang-Ho Hospital between March 2015 and October 2019. Characteristics of subjects, including age, gender, body mass index (BMI), neck circumference, and waist circumference, were obtained from a questionnaire. Pearson regression was used to evaluate the correlations between body profiles and sleep stages as well as sleep disorder indexes. To develop an age and gender independent model, random forests (RF), which is an ensemble learning method with high explainability, were trained by the four groups by gender and age (older or younger than 50 years old) with ratios of 70% (training dataset) and 30% (testing dataset), respectively. Prediction performance was evaluated by sensitivity, specificity and accuracy. Variable importance was assessed by averaging the impurity decrease to account for the effect of different factors. Results Results indicate that high BMI, neck circumference and waist circumference decreased the duration of slow-wave sleep and increased the sleep disorder indices and the percentage of wake and N1. Additionally, screening models for different gender and age utilizing anthropometric features as predictors via RF were established and demonstrated to have high accuracy (75.63% for younger males, 74.72% for elder males, 78.81% for younger females, and 72.10% for elder females). Feature importance indicated that waist circumference was the highest contributing factor in females and elder males, whereas the BMI was the highest contribution in younger males. Conclusions The authors recommend the use of the prediction models for those with Han-Taiwanese craniofacial features.

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A prediction model based on an artificial intelligence system for moderate to severe obstructive sleep apnea

Cited by 40 publications

References 15 publications

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

A prediction model based on artificial neural networks for the diagnosis of obstructive sleep apnea

Risk Screening of Obstructive Sleep Apnea Syndrome by Body Profiles via Random Forests Model

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