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Yang TH, Chen YF, Cheng YF, Huang JN, Wu CS, Chu YC. Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S. BioData Min 2023; 16:35. [PMID: 38098102 PMCID: PMC10722728 DOI: 10.1186/s13040-023-00351-z] [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: 11/04/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023] Open
Abstract
OBJECTIVES The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. METHODS We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model-which had the greatest AUC of 0.83 (95% CI 0.81-0.85)-was then only used on the holdout testing data. RESULTS On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79-0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques-specifically, the LGBM classifier-with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. CONCLUSIONS Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.
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Affiliation(s)
- Tzong-Hann Yang
- Department of Otorhinolaryngology, Taipei City Hospital, Taipei, 100, Taiwan
- General Education Center, University of Taipei, Taipei, 10671, Taiwan
- Department of Speech-Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, 112303, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Fu Chen
- Department of Speech-Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, 112303, Taiwan
| | - Yen-Fu Cheng
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, 112, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, Taipei, 112, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Jue-Ni Huang
- Information Management Office, Taipei Veterans General Hospital, Taipei, 112, Taiwan
| | - Chuan-Song Wu
- Department of Otorhinolaryngology, Taipei City Hospital, Taipei, 100, Taiwan.
- College of Science and Engineering, Fu Jen University, Taipei, 243, Taiwan.
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, Taipei, 112, Taiwan.
- Big Data Center, Taipei Veterans General Hospital, Taipei, 112, Taiwan.
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, 112, Taiwan.
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Tang SY, Chen TH, Kuo KL, Huang JN, Kuo CT, Chu YC. Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study. J Chin Med Assoc 2023; 86:1020-1027. [PMID: 37713313 DOI: 10.1097/jcma.0000000000000994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients. METHODS A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost), light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts. RESULTS Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptor-binding domain antibodies. CONCLUSION This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.
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Affiliation(s)
- Shao-Yu Tang
- Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC
| | - Tz-Heng Chen
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Institute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Ko-Lin Kuo
- Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC
| | - Jue-Ni Huang
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Chen-Tsung Kuo
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Big Data Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan, ROC
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Big Data Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan, ROC
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