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Dai R, Yang K, Zhuang J, Yao L, Hu Y, Chen Q, Zheng H, Zhu X, Ke J, Zeng Y, Fan C, Chen X, Fan J, Zhang Y. Enhanced machine learning approaches for OSA patient screening: model development and validation study. Sci Rep 2024; 14:19756. [PMID: 39187569 PMCID: PMC11347604 DOI: 10.1038/s41598-024-70647-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 08/20/2024] [Indexed: 08/28/2024] Open
Abstract
Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7-84.1%), 69.9% (95% CI 69.8-69.9%), 86.5% (95% CI 81.6-91.3%), 61.5% (95% CI 56.6-66.4%), 53.2% (95% CI 47.7-58.7%), 65.9% (95% CI 60.2-71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.
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Affiliation(s)
- Rongrong Dai
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
- The School of Public Health, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Kang Yang
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, 350108, Fujian, China
- Department of Neurosurgery, National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jiajing Zhuang
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Ling Yao
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Yiming Hu
- The School of Public Health, Fujian Medical University, Fuzhou, 350108, Fujian, China
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qingquan Chen
- The School of Public Health, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Huaxian Zheng
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Xi Zhu
- The School of Public Health, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Jianfeng Ke
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
| | - Yifu Zeng
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510030, Guangdong, China
| | - Chunmei Fan
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
| | - Xiaoyang Chen
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
| | - Jimin Fan
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
| | - Yixiang Zhang
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China.
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China.
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Li E, Ai F, Liang C. A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study. Front Public Health 2024; 11:1348803. [PMID: 38259742 PMCID: PMC10800603 DOI: 10.3389/fpubh.2023.1348803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 12/22/2023] [Indexed: 01/24/2024] Open
Abstract
Objective Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model for depression, aiming to predict the probability of depression in the OSAHS population. Relevant features were analyzed and a nomogram was drawn to visually predict and easily estimate the risk of depression according to the best performing model. Study design This is a cross-sectional study. Methods Data from three cycles (2005-2006, 2007-2008, and 2015-2016) were selected from the NHANES database, and 16 influencing factors were screened and included. Three prediction models were established by the logistic regression algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and random forest algorithm, respectively. The receiver operating characteristic (ROC) area under the curve (AUC), specificity, sensitivity, and decision curve analysis (DCA) were used to assess evaluate and compare the different ML models. Results The logistic regression model had lower sensitivity than the lasso model, while the specificity and AUC area were higher than the random forest and lasso models. Moreover, when the threshold probability range was 0.19-0.25 and 0.45-0.82, the net benefit of the logistic regression model was the largest. The logistic regression model clarified the factors contributing to depression, including gender, general health condition, body mass index (BMI), smoking, OSAHS severity, age, education level, ratio of family income to poverty (PIR), and asthma. Conclusion This study developed three machine learning (ML) models (logistic regression model, lasso model, and random forest model) using the NHANES database to predict depression and identify influencing factors among OSAHS patients. Among them, the logistic regression model was superior to the lasso and random forest models in overall prediction performance. By drawing the nomogram and applying it to the sleep testing center or sleep clinic, sleep technicians and medical staff can quickly and easily identify whether OSAHS patients have depression to carry out the necessary referral and psychological treatment.
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Affiliation(s)
| | | | - Chunguang Liang
- Department of Nursing, Jinzhou Medical University, Jinzhou, China
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Bellini V, Valente M, Turetti M, Del Rio P, Saturno F, Maffezzoni M, Bignami E. Current Applications of Artificial Intelligence in Bariatric Surgery. Obes Surg 2022; 32:2717-2733. [PMID: 35616768 PMCID: PMC9273529 DOI: 10.1007/s11695-022-06100-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 11/27/2022]
Abstract
The application of artificial intelligence technologies is growing in several fields of healthcare settings. The aim of this article is to review the current applications of artificial intelligence in bariatric surgery. We performed a review of the literature on Scopus, PubMed and Cochrane databases, screening all relevant studies published until September 2021, and finally including 36 articles. The use of machine learning algorithms in bariatric surgery is explored in all steps of the clinical pathway, from presurgical risk-assessment and intraoperative management to complications and outcomes prediction. The models showed remarkable results helping physicians in the decision-making process, thus improving the quality of care, and contributing to precision medicine. Several legal and ethical hurdles should be overcome before these methods can be used in common practice.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Melania Turetti
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Francesco Saturno
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Massimo Maffezzoni
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
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Ferreira-Santos D, Amorim P, Silva Martins T, Monteiro-Soares M, Pereira Rodrigues P. Helping early obstructive sleep apnea diagnosis with machine learning: A systematic review (Preprint). J Med Internet Res 2022; 24:e39452. [PMID: 36178720 PMCID: PMC9568812 DOI: 10.2196/39452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/20/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Background American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard. Objective We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA. Methods We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study. Results Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression. Conclusions Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition. Trial Registration PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339
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Affiliation(s)
- Daniela Ferreira-Santos
- Department of Community Medicine, Information and Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
- Center for Health Technology and Services Research, Porto, Portugal
| | - Pedro Amorim
- Department of Community Medicine, Information and Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
- Center for Health Technology and Services Research, Porto, Portugal
- Sleep and Non-Invasive Ventilation Unit, São João University Hospital, Porto, Portugal
| | | | - Matilde Monteiro-Soares
- Department of Community Medicine, Information and Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
- Center for Health Technology and Services Research, Porto, Portugal
- Portuguese Red Cross Health School Lisbon, Lisbon, Portugal
| | - Pedro Pereira Rodrigues
- Department of Community Medicine, Information and Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
- Center for Health Technology and Services Research, Porto, Portugal
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