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Hsu WH, Yang CC, Tsai CY, Majumdar A, Lee KY, Feng PH, Tseng CH, Chen KY, Kang JH, Lee HC, Wu CJ, Kuan YC, Liu WT. Association of Low Arousal Threshold Obstructive Sleep Apnea Manifestations with Body Fat and Water Distribution. Life (Basel) 2023; 13:life13051218. [PMID: 37240863 DOI: 10.3390/life13051218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/20/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E-I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E-I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.
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Affiliation(s)
- Wen-Hua Hsu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
| | - Cheng-Chang Yang
- Department of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
- Brain and Consciousness Research Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
- International Ph.D. Program in Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110301, Taiwan
| | - Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
| | - Arnab Majumdar
- Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
| | - Po-Hao Feng
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
| | - Chien-Hua Tseng
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
| | - Kuan-Yuan Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
| | - Jiunn-Horng Kang
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110301, Taiwan
- Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110301, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei 110301, Taiwan
| | - Cheng-Jung Wu
- Department of Otolaryngology, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
| | - Yi-Chun Kuan
- Department of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei 110301, Taiwan
- Dementia Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
- Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
| | - Wen-Te Liu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110301, Taiwan
- Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan
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Kha QH, Le VH, Hung TNK, Nguyen NTK, Le NQK. Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures. SENSORS (BASEL, SWITZERLAND) 2023; 23:3962. [PMID: 37112302 PMCID: PMC10143839 DOI: 10.3390/s23083962] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 03/26/2023] [Accepted: 04/12/2023] [Indexed: 06/19/2023]
Abstract
Possible drug-food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug-drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament's effect, the withdrawals of various medications, and harmful impacts on the patients' health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug-food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.
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Affiliation(s)
- Quang-Hien Kha
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan
| | - Viet-Huan Le
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan
- Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang City 65000, Vietnam
| | | | - Ngan Thi Kim Nguyen
- Undergraduate Program of Nutrition Science, National Taiwan Normal University, Taipei 106, Taiwan
| | - Nguyen Quoc Khanh Le
- AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
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Tsai CY, Liu WT, Hsu WH, Majumdar A, Stettler M, Lee KY, Cheng WH, Wu D, Lee HC, Kuan YC, Wu CJ, Lin YC, Ho SC. Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events. Digit Health 2023; 9:20552076231152751. [PMID: 36896329 PMCID: PMC9989412 DOI: 10.1177/20552076231152751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 01/04/2023] [Indexed: 03/08/2023] Open
Abstract
Objectives Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.
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Affiliation(s)
- Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Wen-Te Liu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wen-Hua Hsu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Arnab Majumdar
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Marc Stettler
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wun-Hao Cheng
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Dean Wu
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan.,Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yi-Chun Kuan
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan.,Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Cheng-Jung Wu
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Yi-Chih Lin
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Shu-Chuan Ho
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
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Kuo CF, Tsai CY, Cheng WH, Hs WH, Majumdar A, Stettler M, Lee KY, Kuan YC, Feng PH, Tseng CH, Chen KY, Kang JH, Lee HC, Wu CJ, Liu WT. Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles. Digit Health 2023; 9:20552076231205744. [PMID: 37846406 PMCID: PMC10576931 DOI: 10.1177/20552076231205744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/18/2023] [Indexed: 10/18/2023] Open
Abstract
Objective Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. Methods Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.
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Affiliation(s)
- Chih-Fan Kuo
- School of Medicine, China Medical University, Taichung City, Taichung, Taiwan
- Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan
- Department of Medical Education, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Wun-Hao Cheng
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Respiratory Therapy, Division of Pulmonary Medicine, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Wen-Hua Hs
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Arnab Majumdar
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Marc Stettler
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan
| | - Yi-Chun Kuan
- Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Department of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
| | - Po-Hao Feng
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan
| | - Chien-Hua Tseng
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan
| | - Kuan-Yuan Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Jiunn-Horng Kang
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Cheng-Jung Wu
- Department of Otolaryngology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Wen-Te Liu
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
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