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Chen MS, Liu TC, Jhou MJ, Yang CT, Lu CJ. Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b. Diagnostics (Basel) 2024; 14:825. [PMID: 38667472 PMCID: PMC11048899 DOI: 10.3390/diagnostics14080825] [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: 02/27/2024] [Revised: 04/12/2024] [Accepted: 04/13/2024] [Indexed: 04/28/2024] Open
Abstract
Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.
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Affiliation(s)
- Ming-Shu Chen
- Department of Healthcare Administration, College of Healthcare & Management, Asia Eastern University of Science and Technology, New Taipei City 220, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan
| | - Chih-Te Yang
- Department of Business Administration, Tamkang University, New Taipei City 251, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 242, Taiwan
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Taha MA, Morren JA. The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions. Muscle Nerve 2024; 69:260-272. [PMID: 38151482 DOI: 10.1002/mus.28023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/04/2023] [Accepted: 12/09/2023] [Indexed: 12/29/2023]
Abstract
The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.
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Affiliation(s)
- Mohamed A Taha
- Neuromuscular Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - John A Morren
- Neuromuscular Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
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Chang CC, Yeh JH, Chiu HC, Liu TC, Chen YM, Jhou MJ, Lu CJ. Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach. Front Neurol 2023; 14:1283214. [PMID: 38156090 PMCID: PMC10752965 DOI: 10.3389/fneur.2023.1283214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023] Open
Abstract
Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.
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Affiliation(s)
- Che-Cheng Chang
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan
- PhD Program in Nutrition and Food Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Jiann-Horng Yeh
- School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei City, Taiwan
- Department of Neurology, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Hou-Chang Chiu
- School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Neurology, Taipei Medical University, Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Yen-Ming Chen
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan
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Wang CK, Chang CY, Chu TW, Liang YJ. Using Machine Learning to Identify the Relationships between Demographic, Biochemical, and Lifestyle Parameters and Plasma Vitamin D Concentration in Healthy Premenopausal Chinese Women. Life (Basel) 2023; 13:2257. [PMID: 38137858 PMCID: PMC10744461 DOI: 10.3390/life13122257] [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: 10/23/2023] [Revised: 11/15/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
INTRODUCTION Vitamin D plays a vital role in maintaining homeostasis and enhancing the absorption of calcium, an essential component for strengthening bones and preventing osteoporosis. There are many factors known to relate to plasma vitamin D concentration (PVDC). However, most of these studies were performed with traditional statistical methods. Nowadays, machine learning methods (Mach-L) have become new tools in medical research. In the present study, we used four Mach-L methods to explore the relationships between PVDC and demographic, biochemical, and lifestyle factors in a group of healthy premenopausal Chinese women. Our goals were as follows: (1) to evaluate and compare the predictive accuracy of Mach-L and MLR, and (2) to establish a hierarchy of the significance of the aforementioned factors related to PVDC. METHODS Five hundred ninety-three healthy Chinese women were enrolled. In total, there were 35 variables recorded, including demographic, biochemical, and lifestyle information. The dependent variable was 25-OH vitamin D (PVDC), and all other variables were the independent variables. Multiple linear regression (MLR) was regarded as the benchmark for comparison. Four Mach-L methods were applied (random forest (RF), stochastic gradient boosting (SGB), extreme gradient boosting (XGBoost), and elastic net). Each method would produce several estimation errors. The smaller these errors were, the better the model was. RESULTS Pearson's correlation, age, glycated hemoglobin, HDL-cholesterol, LDL-cholesterol, and hemoglobin were positively correlated to PVDC, whereas eGFR was negatively correlated to PVDC. The Mach-L methods yielded smaller estimation errors for all five parameters, which indicated that they were better methods than the MLR model. After averaging the importance percentage from the four Mach-L methods, a rank of importance could be obtained. Age was the most important factor, followed by plasma insulin level, TSH, spouse status, LDH, and ALP. CONCLUSIONS In a healthy Chinese premenopausal cohort using four different Mach-L methods, age was found to be the most important factor related to PVDC, followed by plasma insulin level, TSH, spouse status, LDH, and ALP.
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Affiliation(s)
- Chun-Kai Wang
- Department of Obstetrics and Gynecology, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung 813, Taiwan;
| | - Ching-Yao Chang
- Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan;
| | - Ta-Wei Chu
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Chief Executive Officer’s Office, MJ Health Research Foundation, Taipei 114, Taiwan;
| | - Yao-Jen Liang
- Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan;
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Yang CC, Peng CH, Huang LY, Chen FY, Kuo CH, Wu CZ, Hsia TL, Lin CY. Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes. World J Clin Cases 2023; 11:7951-7964. [DOI: 10.12998/wjcc.v11.i33.7951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/23/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND The prevalence of type 2 diabetes (T2D) has been increasing dramatically in recent decades, and 47.5% of T2D patients will die of cardiovascular disease. Thallium-201 myocardial perfusion scan (MPS) is a precise and non-invasive method to detect coronary artery disease (CAD). Most previous studies used traditional logistic regression (LGR) to evaluate the risks for abnormal CAD. Rapidly developing machine learning (Mach-L) techniques could potentially outperform LGR in capturing non-linear relationships.
AIM To aims were: (1) Compare the accuracy of Mach-L methods and LGR; and (2) Found the most important factors for abnormal TMPS.
METHODS 556 T2D were enrolled in the study (287 men and 269 women). Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable. Subjects with a MPS score ≥ 9 were defined as abnormal. In addition to traditional LGR, classification and regression tree (CART), random forest, Naïve Bayes, and eXtreme gradient boosting were also applied. Sensitivity, specificity, accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods.
RESULTS Except for CART, the other Mach-L methods outperformed LGR, with gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS.
CONCLUSION Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D, with the most important risk factors being gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking.
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Affiliation(s)
- Chung-Chi Yang
- Division of Cardiovascular Medicine, Taoyuan Armed Forces General Hospital, Taoyuan City 32551, Taiwan
- Division of Cardiovascular, Tri-service General Hospital, Taipei City 114202, Taiwan
| | - Chung-Hsin Peng
- Department of Urology, Cardinal Tien Hospital, New Taipei City 23148, Taiwan
- School of Medicine, Fu-Jen Catholic University, New Taipei City 242062, Taiwan
| | - Li-Ying Huang
- Department of Internal Medicine, Department of Medical Education, School of Medicine, Fu Jen Catholic University Hospital, New Taipei City 243, Taiwan
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 243, Taiwan
| | - Fang Yu Chen
- Department of Endocrinology, Fu Jen Catholic University Hospital, New Taipei City 243, Taiwan
| | - Chun-Heng Kuo
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 243, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei City 243, Taiwan
| | - Chung-Ze Wu
- Division of Endocrinology, Shuang Ho Hospital, New Taipei City 23561, Taiwan
- School of Medicine, Taipei Medical University, Taipei City 11031, Taiwan
| | - Te-Lin Hsia
- Department of Internal Medicine, Cardinal Tien Hospital, New Taipei City 23148, Taiwan
| | - Chung-Yu Lin
- Department of Cardiology, Fu Jen Catholic University Hospital, New Taipei City 24352, Taiwan
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
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Tzou SJ, Peng CH, Huang LY, Chen FY, Kuo CH, Wu CZ, Chu TW. Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort. J Chin Med Assoc 2023; 86:1028-1036. [PMID: 37729604 DOI: 10.1097/jcma.0000000000000999] [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: 09/22/2023] Open
Abstract
BACKGROUND Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density [BMD] or fracture rate) as a categorical one, which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of BMD, seeking to (1) compare the prediction accuracy between different machine learning methods and traditional multiple linear regression (MLR) and (2) rank the importance of 25 different risk factors. METHODS The study sample includes 24 412 women older than 55 years with 25 related variables, applying traditional MLR and five different machine learning methods: classification and regression tree, Naïve Bayes, random forest, stochastic gradient boosting, and eXtreme gradient boosting. The metrics used for model performance comparisons are the symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error. RESULTS Machine learning approaches outperformed MLR for all four prediction errors. The average importance ranking of each factor generated by the machine learning methods indicates that age is the most important factor determining T-score, followed by estimated glomerular filtration rate (eGFR), body mass index (BMI), uric acid (UA), and education level. CONCLUSION In a group of women older than 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level.
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Affiliation(s)
- Shiow-Jyu Tzou
- Teaching and Researching Center, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan, ROC
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC
| | - Chung-Hsin Peng
- Department of Urology, Cardinal Tien Hospital, School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan, ROC
| | - Li-Ying Huang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei, Taiwan
| | - Fang-Yu Chen
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei, Taiwan
| | - Chun-Heng Kuo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei, Taiwan
| | - Chung-Ze Wu
- Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Division of Endocrinology and Metabolism, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, ROC
| | - Ta-Wei Chu
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
- MJ Health Research Foundation, Taipei, Taiwan, ROC
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Chen CH, Wang CK, Wang CY, Chang CF, Chu TW. Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese. World J Clin Cases 2023; 11:7004-7016. [PMID: 37946770 PMCID: PMC10631406 DOI: 10.12998/wjcc.v11.i29.7004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/01/2023] [Accepted: 09/11/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND The incidence of chronic kidney disease (CKD) has dramatically increased in recent years, with significant impacts on patient mortality rates. Previous studies have identified multiple risk factors for CKD, but they mostly relied on the use of traditional statistical methods such as logistic regression and only focused on a few risk factors. AIM To determine factors that can be used to identify subjects with a low estimated glomerular filtration rate (L-eGFR < 60 mL/min per 1.73 m2) in a cohort of 1236 Chinese people aged over 65. METHODS Twenty risk factors were divided into three models. Model 1 consisted of demographic and biochemistry data. Model 2 added lifestyle data to Model 1, and Model 3 added inflammatory markers to Model 2. Five machine learning methods were used: Multivariate adaptive regression splines, eXtreme Gradient Boosting, stochastic gradient boosting, Light Gradient Boosting Machine, and Categorical Features + Gradient Boosting. Evaluation criteria included accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), F-1 score, and balanced accuracy. RESULTS A trend of increasing AUC of each was observed from Model 1 to Model 3 and reached statistical significance. Model 3 selected uric acid as the most important risk factor, followed by age, hemoglobin (Hb), body mass index (BMI), sport hours, and systolic blood pressure (SBP). CONCLUSION Among all the risk factors including demographic, biochemistry, and lifestyle risk factors, along with inflammation markers, UA is the most important risk factor to identify L-eGFR, followed by age, Hb, BMI, sport hours, and SBP in a cohort of elderly Chinese people.
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Affiliation(s)
- Chao-Hung Chen
- Division of Urology, Department of Surgery, Kaohsiung Armed Forces General Hospital, Kaohsiung 802, Taiwan
- Division of Urology, Department of Surgery, Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Chun-Kai Wang
- Department of Obstetrics and Gynecology, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung 813, Taiwan
| | - Chen-Yu Wang
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Chun-Feng Chang
- Division of Urology, Department of Surgery, Kaohsiung Armed Forces General Hospital, Kaohsiung 802, Taiwan
- Division of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Ta-Wei Chu
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
- Chief Executive Officer's Office, MJ Health Research Foundation, Taipei 114, Taiwan
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Tsai MH, Jhou MJ, Liu TC, Fang YW, Lu CJ. An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages. Front Med (Lausanne) 2023; 10:1155426. [PMID: 37859858 PMCID: PMC10582636 DOI: 10.3389/fmed.2023.1155426] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/19/2023] [Indexed: 10/21/2023] Open
Abstract
Background and objectives Chronic kidney disease (CKD) is a global health concern. This study aims to identify key factors associated with renal function changes using the proposed machine learning and important variable selection (ML&IVS) scheme on longitudinal laboratory data. The goal is to predict changes in the estimated glomerular filtration rate (eGFR) in a cohort of patients with CKD stages 3-5. Design A retrospective cohort study. Setting and participants A total of 710 outpatients who presented with stable nondialysis-dependent CKD stages 3-5 at the Shin-Kong Wu Ho-Su Memorial Hospital Medical Center from 2016 to 2021. Methods This study analyzed trimonthly laboratory data including 47 indicators. The proposed scheme used stochastic gradient boosting, multivariate adaptive regression splines, random forest, eXtreme gradient boosting, and light gradient boosting machine algorithms to evaluate the important factors for predicting the results of the fourth eGFR examination, especially in patients with CKD stage 3 and those with CKD stages 4-5, with or without diabetes mellitus (DM). Main outcome measurement Subsequent eGFR level after three consecutive laboratory data assessments. Results Our ML&IVS scheme demonstrated superior predictive capabilities and identified significant factors contributing to renal function changes in various CKD groups. The latest levels of eGFR, blood urea nitrogen (BUN), proteinuria, sodium, and systolic blood pressure as well as mean levels of eGFR, BUN, proteinuria, and triglyceride were the top 10 significantly important factors for predicting the subsequent eGFR level in patients with CKD stages 3-5. In individuals with DM, the latest levels of BUN and proteinuria, mean levels of phosphate and proteinuria, and variations in diastolic blood pressure levels emerged as important factors for predicting the decline of renal function. In individuals without DM, all phosphate patterns and latest albumin levels were found to be key factors in the advanced CKD group. Moreover, proteinuria was identified as an important factor in the CKD stage 3 group without DM and CKD stages 4-5 group with DM. Conclusion The proposed scheme highlighted factors associated with renal function changes in different CKD conditions, offering valuable insights to physicians for raising awareness about renal function changes.
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Affiliation(s)
- Ming-Hsien Tsai
- Division of Nephrology, Department of Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- Department of Medicine, School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Yu-Wei Fang
- Division of Nephrology, Department of Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- Department of Medicine, School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan
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Huang EJC, Wu MH, Wang TJ, Huang TJ, Li YR, Lee CY. Myasthenia Gravis: Novel Findings and Perspectives on Traditional to Regenerative Therapeutic Interventions. Aging Dis 2023; 14:1070-1092. [PMID: 37163445 PMCID: PMC10389825 DOI: 10.14336/ad.2022.1215] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/15/2022] [Indexed: 05/12/2023] Open
Abstract
The prevalence of myasthenia gravis (MG), an autoimmune disorder, is increasing among all subsets of the population leading to an elevated economic and social burden. The pathogenesis of MG is characterized by the synthesis of autoantibodies against the acetylcholine receptor (AChR), low-density lipoprotein receptor-related protein 4 (LRP4), or muscle-specific kinase at the neuromuscular junction, thereby leading to muscular weakness and fatigue. Based on clinical and laboratory examinations, the research is focused on distinguishing MG from other autoimmune, genetic diseases of neuromuscular transmission. Technological advancements in machine learning, a subset of artificial intelligence (AI) have been assistive in accurate diagnosis and management. Besides, addressing the clinical needs of MG patients is critical to improving quality of life (QoL) and satisfaction. Lifestyle changes including physical exercise and traditional Chinese medicine/herbs have also been shown to exert an ameliorative impact on MG progression. To achieve enhanced therapeutic efficacy, cholinesterase inhibitors, immunosuppressive drugs, and steroids in addition to plasma exchange therapy are widely recommended. Under surgical intervention, thymectomy is the only feasible alternative to removing thymoma to overcome thymoma-associated MG. Although these conventional and current therapeutic approaches are effective, the associated adverse events and surgical complexity limit their wide application. Moreover, Restivo et al. also, to increase survival and QoL, further recent developments revealed that antibody, gene, and regenerative therapies (such as stem cells and exosomes) are currently being investigated as a safer and more efficacious alternative. Considering these above-mentioned points, we have comprehensively reviewed the recent advances in pathological etiologies of MG including COVID-19, and its therapeutic management.
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Affiliation(s)
- Evelyn Jou-Chen Huang
- Department of Ophthalmology, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Ophthalmology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Meng-Huang Wu
- Department of Orthopedics, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Orthopaedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Tsung-Jen Wang
- Department of Ophthalmology, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Ophthalmology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Tsung-Jen Huang
- Department of Orthopedics, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Orthopaedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Yan-Rong Li
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital and College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Ching-Yu Lee
- Department of Orthopedics, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Orthopaedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
- International PhD Program for Cell Therapy and Regeneration Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.
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Zhong H, Ruan Z, Yan C, Lv Z, Zheng X, Goh LY, Xi J, Song J, Luo L, Chu L, Tan S, Zhang C, Bu B, Da Y, Duan R, Yang H, Luo S, Chang T, Zhao C. Short-term outcome prediction for myasthenia gravis: an explainable machine learning model. Ther Adv Neurol Disord 2023; 16:17562864231154976. [PMID: 36860354 PMCID: PMC9969443 DOI: 10.1177/17562864231154976] [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: 05/17/2022] [Accepted: 01/15/2023] [Indexed: 02/26/2023] Open
Abstract
Background Myasthenia gravis (MG) is an autoimmune disease characterized by muscle weakness and fatigability. The fluctuating nature of the disease course impedes the clinical management. Objective The purpose of the study was to establish and validate a machine learning (ML)-based model for predicting the short-term clinical outcome in MG patients with different antibody types. Methods We studied 890 MG patients who had regular follow-ups at 11 tertiary centers in China from 1 January 2015 to 31 July 2021 (653 patients for derivation and 237 for validation). The short-term outcome was the modified post-intervention status (PIS) at a 6-month visit. A two-step variable screening was used to determine the factors for model construction and 14 ML algorithms were used for model optimisation. Results The derivation cohort included 653 patients from Huashan hospital [age 44.24 (17.22) years, female 57.6%, generalized MG 73.5%], and the validation cohort included 237 patients from 10 independent centers [age 44.24 (17.22) years, female 55.0%, generalized MG 81.2%]. The ML model identified patients who were improved with an area under the receiver operating characteristic curve (AUC) of 0.91 [0.89-0.93], 'Unchanged' 0.89 [0.87-0.91], and 'Worse' 0.89 [0.85-0.92] in the derivation cohort, whereas identified patients who were improved with an AUC of 0.84 [0.79-0.89], 'Unchanged' 0.74 [0.67-0.82], and 'Worse' 0.79 [0.70-0.88] in the validation cohort. Both datasets presented a good calibration ability by fitting the expectation slopes. The model is finally explained by 25 simple predictors and transferred to a feasible web tool for an initial assessment. Conclusion The explainable, ML-based predictive model can aid in forecasting the short-term outcome for MG with good accuracy in clinical practice.
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Affiliation(s)
| | | | | | - Zhiguo Lv
- Department of Neurology, The Affiliated
Hospital of Changchun University of Chinese Medicine, Changchun, China
| | - Xueying Zheng
- Department of Biostatistics, School of Public
Health and Key Laboratory of Public Health Safety, Fudan University,
Shanghai, China
| | - Li-Ying Goh
- Shanghai Medical College, Fudan University,
Shanghai, China
| | - Jianying Xi
- Huashan Rare Disease Center, Department of
Neurology, Huashan Hospital, Fudan University, Shanghai, China,National Center for Neurological Disorders,
Shanghai, China
| | - Jie Song
- Huashan Rare Disease Center, Department of
Neurology, Huashan Hospital, Fudan University, Shanghai, China,National Center for Neurological Disorders,
Shanghai, China
| | - Lijun Luo
- Department of Neurology, Wuhan No.1 Hospital,
Wuhan, China
| | - Lan Chu
- Department of Neurology, The Affiliated
Hospital of Guizhou Medical University, Guiyang, China
| | - Song Tan
- Department of Neurology, Sichuan Provincial
People’s Hospital, University of Electronic Science and Technology of China,
Chengdu, China
| | - Chao Zhang
- Department of Neurology and Tianjin
Neurological Institute, Tianjin Medical University General Hospital,
Tianjin, China
| | - Bitao Bu
- Department of Neurology, Tongji Hospital,
Tongji Medical College, Huazhong University of Science and Technology,
Wuhan, China
| | - Yuwei Da
- Department of Neurology, Xuanwu Hospital,
Capital Medical University, Beijing, China
| | - Ruisheng Duan
- Department of Neurology, The First Affiliated
Hospital of Shandong First Medical University, Jinan, China
| | - Huan Yang
- Department of Neurology, Xiangya Hospital,
Central South University, Changsha, China
| | | | - Ting Chang
- Department of Neurology, Tangdu Hospital, The
Air Force Medical University, Xi’an 710000, China
| | - Chongbo Zhao
- Huashan Rare Disease Center, Department of
Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China,National Center for Neurological Disorders,
Shanghai, China
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11
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Huang HH, Hsieh SJ, Chen MS, Jhou MJ, Liu TC, Shen HL, Yang CT, Hung CC, Yu YY, Lu CJ. Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators. J Clin Med 2023; 12:1220. [PMID: 36769868 PMCID: PMC9917545 DOI: 10.3390/jcm12031220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/13/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
In many countries, especially developed nations, the fertility rate and birth rate have continually declined. Taiwan's fertility rate has paralleled this trend and reached its nadir in 2022. Therefore, the government uses many strategies to encourage more married couples to have children. However, couples marrying at an older age may have declining physical status, as well as hypertension and other metabolic syndrome symptoms, in addition to possibly being overweight, which have been the focus of the studies for their influences on male and female gamete quality. Many previous studies based on infertile people are not truly representative of the general population. This study proposed a framework using five machine learning (ML) predictive algorithms-random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting-to identify the major risk factors affecting male sperm count based on a major health screening database in Taiwan. Unlike traditional multiple linear regression, ML algorithms do not need statistical assumptions and can capture non-linear relationships or complex interactions between dependent and independent variables to generate promising performance. We analyzed annual health screening data of 1375 males from 2010 to 2017, including data on health screening indicators, sourced from the MJ Group, a major health screening center in Taiwan. The symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error were used as performance evaluation metrics. Our results show that sleep time (ST), alpha-fetoprotein (AFP), body fat (BF), systolic blood pressure (SBP), and blood urea nitrogen (BUN) are the top five risk factors associated with sperm count. ST is a known risk factor influencing reproductive hormone balance, which can affect spermatogenesis and final sperm count. BF and SBP are risk factors associated with metabolic syndrome, another known risk factor of altered male reproductive hormone systems. However, AFP has not been the focus of previous studies on male fertility or semen quality. BUN, the index for kidney function, is also identified as a risk factor by our established ML model. Our results support previous findings that metabolic syndrome has negative impacts on sperm count and semen quality. Sleep duration also has an impact on sperm generation in the testes. AFP and BUN are two novel risk factors linked to sperm counts. These findings could help healthcare personnel and law makers create strategies for creating environments to increase the country's fertility rate. This study should also be of value to follow-up research.
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Affiliation(s)
- Hung-Hsiang Huang
- Division of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei City 220, Taiwan
| | - Shang-Ju Hsieh
- Division of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei City 220, Taiwan
| | - Ming-Shu Chen
- Department of Healthcare Administration, College of Healthcare & Management, Asia Eastern University of Science and Technology, New Taipei City 220, Taiwan
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan
| | - Hsiang-Li Shen
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan
| | - Chih-Te Yang
- Department of Business Administration, Tamkang University, New Taipei City 251, Taiwan
| | - Chung-Chih Hung
- Department of Laboratory Medicine, Taipei Hospital, Ministry of Health and Welfare, New Taipei City 242, Taiwan
| | - Ya-Yen Yu
- Department of Medical Laboratory, Chang-Hua Hospital, Ministry of Health and Welfare, Chang Hua County 513, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 242, Taiwan
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Huang YC, Cheng YC, Jhou MJ, Chen M, Lu CJ. Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2359. [PMID: 36767726 PMCID: PMC9915180 DOI: 10.3390/ijerph20032359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/24/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
The new generation of nonvitamin K antagonists are broadly applied for stroke prevention due to their notable efficacy and safety. Our study aimed to develop a suggestive utilization of dabigatran through an integrated machine learning (ML) decision-tree model. Participants taking different doses of dabigatran in the Randomized Evaluation of Long-Term Anticoagulant Therapy trial were included in our analysis and defined as the 110 mg and 150 mg groups. The proposed scheme integrated ML methods, namely naive Bayes, random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost), which were used to identify the essential variables for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. RF (0.764 for 110 mg; 0.747 for 150 mg) and XGBoost (0.708 for 110 mg; 0.761 for 150 mg) had better area under the receiver operating characteristic curve (AUC) values than logistic regression (benchmark model; 0.683 for 110 mg; 0.739 for 150 mg). We then selected the top ten important variables as internal nodes of the CART decision tree. The two best CART models with ten important variables output tree-shaped rules for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. Our model can be used to provide more visualized and interpretable suggestive rules to clinicians managing NVAF patients who are taking dabigatran.
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Affiliation(s)
- Yung-Chuan Huang
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan
| | - Yu-Chen Cheng
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Mingchih Chen
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan
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13
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Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol. Diagnostics (Basel) 2022; 12:diagnostics12081965. [PMID: 36010315 PMCID: PMC9407063 DOI: 10.3390/diagnostics12081965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 11/26/2022] Open
Abstract
Purpose: Cardiovascular disease (CVD) is a major worldwide health burden. As the risk factors of CVD, hypertension, and hyperlipidemia are most mentioned. Early stage hypertension in the population with dyslipidemia is an important public health hazard. This study was the application of data-driven machine learning (ML), demonstrating complex relationships between risk factors and outcomes and promising predictive performance with vast amounts of medical data, aimed to investigate the association between dyslipidemia and the incidence of early stage hypertension in a large cohort with normal blood pressure at baseline. Methods: This study analyzed annual health screening data for 71,108 people from 2005 to 2017, including data for 27 risk-related indicators, sourced from the MJ Group, a major health screening center in Taiwan. We used five machine learning (ML) methods—stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), least absolute shrinkage and selection operator regression (Lasso), ridge regression (Ridge), and gradient boosting with categorical features support (CatBoost)—to develop a multi-stage ML algorithm-based prediction scheme and then evaluate important risk factors at the early stage of hypertension, especially for groups with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels within or out of the reference range. Results: Age, body mass index, waist circumference, waist-to-hip ratio, fasting plasma glucose, and C-reactive protein (CRP) were associated with hypertension. The hemoglobin level was also a positive contributor to blood pressure elevation and it appeared among the top three important risk factors in all LDL-C/HDL-C groups; therefore, these variables may be important in affecting blood pressure in the early stage of hypertension. A residual contribution to blood pressure elevation was found in groups with increased LDL-C. This suggests that LDL-C levels are associated with CPR levels, and that the LDL-C level may be an important factor for predicting the development of hypertension. Conclusion: The five prediction models provided similar classifications of risk factors. The results of this study show that an increase in LDL-C is more important than the start of a drop in HDL-C in health screening of sub-healthy adults. The findings of this study should be of value to health awareness raising about hypertension and further discussion and follow-up research.
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14
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Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes. Diagnostics (Basel) 2022; 12:diagnostics12071619. [PMID: 35885524 PMCID: PMC9324130 DOI: 10.3390/diagnostics12071619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 06/27/2022] [Accepted: 06/30/2022] [Indexed: 11/17/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies.
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Huang LY, Chen FY, Jhou MJ, Kuo CH, Wu CZ, Lu CH, Chen YL, Pei D, Cheng YF, Lu CJ. Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin-Creatinine Ratio in a 4-Year Follow-Up Study. J Clin Med 2022; 11:3661. [PMID: 35806944 PMCID: PMC9267784 DOI: 10.3390/jcm11133661] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/19/2022] [Accepted: 06/22/2022] [Indexed: 02/07/2023] Open
Abstract
The urine albumin-creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.
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Affiliation(s)
- Li-Ying Huang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Department of Medical Education, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 24352, Taiwan; (L.-Y.H.); (F.-Y.C.); (C.-H.K.); (D.P.)
| | - Fang-Yu Chen
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Department of Medical Education, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 24352, Taiwan; (L.-Y.H.); (F.-Y.C.); (C.-H.K.); (D.P.)
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan;
| | - Chun-Heng Kuo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Department of Medical Education, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 24352, Taiwan; (L.-Y.H.); (F.-Y.C.); (C.-H.K.); (D.P.)
| | - Chung-Ze Wu
- Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City 23561, Taiwan;
- Division of Endocrinology and Metabolism, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Chieh-Hua Lu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, School of Medicine, National Defense Medical Center, Taipei 11490, Taiwan;
| | - Yen-Lin Chen
- Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan;
| | - Dee Pei
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Department of Medical Education, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 24352, Taiwan; (L.-Y.H.); (F.-Y.C.); (C.-H.K.); (D.P.)
| | - Yu-Fang Cheng
- Department of Endocrinology and Metabolism, Changhua Christian Hospital, Changhua 50051, Taiwan;
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan;
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan
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