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Zhang ZM, Huang Y, Liu G, Yu W, Xie Q, Chen Z, Huang G, Wei J, Zhang H, Chen D, Du H. Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma. Sci Rep 2024; 14:5274. [PMID: 38438393 PMCID: PMC10912761 DOI: 10.1038/s41598-024-51265-7] [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: 10/19/2023] [Accepted: 01/03/2024] [Indexed: 03/06/2024] Open
Abstract
Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/ . Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques.
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Affiliation(s)
- Zi-Mei Zhang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Yuting Huang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Guanghao Liu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, 350122, China
| | - Wenqi Yu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Qingsong Xie
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Zixi Chen
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Guanda Huang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Jinfen Wei
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Haibo Zhang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Dong Chen
- Fangrui Institute of Innovative Drugs, South China University of Technology, Guangzhou, China
| | - Hongli Du
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.
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Hasan MM, Phu J, Sowmya A, Meijering E, Kalloniatis M. Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases. Clin Exp Optom 2024; 107:130-146. [PMID: 37674264 DOI: 10.1080/08164622.2023.2235346] [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: 02/23/2023] [Accepted: 07/07/2023] [Indexed: 09/08/2023] Open
Abstract
Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines. Machine learning and deep learning - two primary data-driven pattern analysis approaches under the umbrella of artificial intelligence - has created considerable interest in the last few decades. The evolution of technology has resulted in a substantial amount of artificial intelligence research on ophthalmic and neurodegenerative disease diagnosis using retinal images. Various artificial intelligence-based techniques have been used for diagnostic purposes, including traditional machine learning, deep learning, and their combinations. Presented here is a review of the literature covering the last 10 years on this topic, discussing the use of artificial intelligence in analysing data from different modalities and their combinations for the diagnosis of glaucoma and neurodegenerative diseases. The performance of published artificial intelligence methods varies due to several factors, yet the results suggest that such methods can potentially facilitate clinical diagnosis. Generally, the accuracy of artificial intelligence-assisted diagnosis ranges from 67-98%, and the area under the sensitivity-specificity curve (AUC) ranges from 0.71-0.98, which outperforms typical human performance of 71.5% accuracy and 0.86 area under the curve. This indicates that artificial intelligence-based tools can provide clinicians with useful information that would assist in providing improved diagnosis. The review suggests that there is room for improvement of existing artificial intelligence-based models using retinal imaging modalities before they are incorporated into clinical practice.
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Affiliation(s)
- Md Mahmudul Hasan
- School of Computer Science and Engineering, University of New South Wales, Kensington, New South Wales, Australia
| | - Jack Phu
- School of Optometry and Vision Science, University of New South Wales, Kensington, Australia
- Centre for Eye Health, University of New South Wales, Sydney, New South Wales, Australia
- School of Medicine (Optometry), Deakin University, Waurn Ponds, Victoria, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, University of New South Wales, Kensington, New South Wales, Australia
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Kensington, New South Wales, Australia
| | - Michael Kalloniatis
- School of Optometry and Vision Science, University of New South Wales, Kensington, Australia
- School of Medicine (Optometry), Deakin University, Waurn Ponds, Victoria, Australia
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Huang X, Islam MR, Akter S, Ahmed F, Kazami E, Serhan HA, Abd-Alrazaq A, Yousefi S. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Biomed Eng Online 2023; 22:126. [PMID: 38102597 PMCID: PMC10725017 DOI: 10.1186/s12938-023-01187-8] [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: 08/09/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
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Affiliation(s)
- Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Md Rafiqul Islam
- Business Information Systems, Australian Institute of Higher Education, Sydney, Australia
| | - Shanjita Akter
- School of Computer Science, Taylors University, Subang Jaya, Malaysia
| | - Fuad Ahmed
- Department of Computer Science & Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - Ehsan Kazami
- Ophthalmology, General Hospital of Mahabad, Urmia University of Medical Sciences, Urmia, Iran
| | - Hashem Abu Serhan
- Department of Ophthalmology, Hamad Medical Corporations, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, USA.
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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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Hwang HS, Lee EJ, Kim H, Kim TW. Relationships of Macular Functional Impairment With Structural and Vascular Changes According to Glaucoma Severity. Invest Ophthalmol Vis Sci 2023; 64:5. [PMID: 37669065 PMCID: PMC10484033 DOI: 10.1167/iovs.64.12.5] [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: 05/23/2023] [Accepted: 08/09/2023] [Indexed: 09/06/2023] Open
Abstract
Purpose To determine the pointwise relationships of central visual field (VF) defects with macular ganglion cell loss and macular vessel density (VD) loss during various stages of glaucoma. Methods Eyes with primary open-angle glaucoma (POAG) were subjected to optical coherence tomography (OCT) and OCT angiography (OCTA) to evaluate macular ganglion cell layer (GCL) thickness and macular VD in the superficial and deep vascular complexes (SVC and DVC). OCT, OCTA, and VF locations were matched after correcting for retinal ganglion cell (RGC) displacement. Pointwise correlations of GCL thickness and VDs of the SVC and DVC with central VF sensitivity (VFS) were evaluated by Pearson's correlation analysis and compared in eyes with early and advanced POAG by Meng's test. Results Of the 100 eyes, 52 and 48 were classified as early and advanced POAG. Macular VD showed overall better correlation with central VFS than GCL thickness in both the early and advanced groups. SVC density showed the strongest correlation with central VFS in all groups (R = 0.327 in early group, R = 0.325 in advanced group, all P < 0.001). Although DVC density showed better correlation with VFS (R = 0.311) than GCL thickness (R = 0.212) in the early group (P < 0.001), the correlation was comparable in the advanced group (R = 0.199 and 0.176, respectively, P = 0.254). Conclusions After adjustment for RGC displacement, macular SVC density was better correlated with central VFS than macular GCL thickness in both early and advanced POAG. Macular DVC density showed better correlation with VFS than GCL thickness in early but not in advanced POAG, indicating that DVC loss may be involved in early central VF loss.
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Affiliation(s)
- Hye Seong Hwang
- Department of Ophthalmology, Chungbuk National University College of Medicine, Chungbuk National University Hospital, Choengju, Korea
| | - Eun Ji Lee
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Hyunjoong Kim
- Department of Applied Statistics, Yonsei University, Seoul, Korea
| | - Tae-Woo Kim
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
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Wu CW, Chang YC, Chen HY. Early Detection of Primary Open Angle, Angle Closure, and Normal Tension Glaucoma in an Asian Population Using Optical Coherence Tomography. J Glaucoma 2023; 32:195-203. [PMID: 36729693 DOI: 10.1097/ijg.0000000000002160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 11/29/2022] [Indexed: 02/03/2023]
Abstract
PRCIS Spectral-domain optical coherence tomography (SD-OCT) facilitates early glaucoma detection in the Chinese population in Taiwan. The best parameters for primary open angle glaucoma (POAG), primary angle closure glaucoma (PACG), normal tension glaucoma (NTG), and suspected glaucoma (GS) detection are temporal inferior Bruch's membrane opening-minimum rim width (BMO-MRW), inner temporal macular ganglion cell layer (mGCL), temporal superior Circumpapillary retinal nerve fiber layer (cpRNFL), and mean global BMO-MRW, respectively. PURPOSE We investigated the diagnostic capability of SD-OCT for different types of early glaucoma among the Chinese population in Taiwan. PARTICIPANTS AND METHODS One eye each was assessed from 113 individuals with healthy eyes, 125 individuals with suspected glaucoma (GS), and 156 patients with early glaucoma (POAG, 87; PACG, 50; and NTG, 19). Circumpapillary (cp) RNFL thickness (global and sectoral), BMO-MRW, and macular parameters, including the macular RNFL (mRNFL), mGCL, and macular inner plexiform layer (mIPL), were assessed using SD-OCT. The areas under receiver operator characteristic curves (AUCs) were calculated to evaluate the diagnostic capacity of the parameters to differentiate between healthy and early glaucomatous eyes. RESULTS The parameters most suitable for detecting early POAG, PACG, NTG, and GS were temporal inferior BMO-MRW (AUC, 0.847), inner temporal mGCL (AUC, 0.770), temporal superior cpRNFL (AUC, 0.861), and mean global BMO-MRW (AUC, 0.768), respectively. Among the macular parameters, the mGCL exhibited the highest diagnostic capacity. The diagnostic capacity of the mGCL was lower than that of cpRNFL and BMO-MRW for POAG and NTG but not PACG. After adjusting for confounding variables in multivariable analysis, the AUC was determined to be 0.935 for POAG and 0.787 for GS. CONCLUSION SD-OCT facilitates the detection of early POAG, PACG, and NTG. Using a combination of cpRNFL, BMO-MRW, and macular parameters may enhance their diagnostic capacities. Further studies are necessary to validate these findings.
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Affiliation(s)
- Chao-Wei Wu
- Graduate Institute of Medicine
- Department of Ophthalmology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung City
| | | | - Hsin-Yi Chen
- School of Medicine, College of Medicine
- Department of Ophthalmology, Fu Jen Catholic University Hospital
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan, Republic of China
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Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators. J Clin Med 2023; 12:jcm12031220. [PMID: 36769868 PMCID: PMC9917545 DOI: 10.3390/jcm12031220] [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: 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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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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Tarcoveanu F, Leon F, Curteanu S, Chiselita D, Bogdanici CM, Anton N. Classification Algorithms Used in Predicting Glaucoma Progression. Healthcare (Basel) 2022; 10:1831. [PMID: 36292278 PMCID: PMC9601916 DOI: 10.3390/healthcare10101831] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 07/25/2023] Open
Abstract
In this paper, various machine learning algorithms were used in order to predict the evolution of open-angle glaucoma (POAG). The datasets were built containing clinical observations and objective measurements made at the Countess of Chester Hospital in the UK and at the "St. Spiridon" Hospital of Iași, Romania. Using these datasets, different classification problems were proposed. The evaluation of glaucoma progression was conducted based on parameters such as VFI (Visual field index), MD (Mean Deviation), PSD (Pattern standard deviation), and RNFL (Retinal Nerve Fiber Layer). As classification tools, the following algorithms were used: Multilayer Perceptron, Random Forest, Random Tree, C4.5, k-Nearest Neighbors, Support Vector Machine, and Non-Nested Generalized Exemplars. The best results, with an accuracy of over 90%, were obtained with Multilayer Perceptron and Random Forest algorithms. The NNGE algorithm also proved very useful in creating a hierarchy of the input values according to their influence (weight) on the considered outputs. On the other hand, the decision tree algorithms gave us insight into the logic used in their classification, which is of practical importance in obtaining additional information regarding the rationale behind a certain rule or decision.
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Affiliation(s)
- Filip Tarcoveanu
- Ophthalmology Department, Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa” Iasi, University Street No 16, 700115 Iasi, Romania
| | - Florin Leon
- Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iasi, 27 Mangeron Street, 700050 Iasi, Romania
| | - Silvia Curteanu
- Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, “Gheorghe Asachi” Technical University of Iasi, 73 Mangeron Street, 700050 Iasi, Romania
| | - Dorin Chiselita
- Ophthalmology Department, Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa” Iasi, University Street No 16, 700115 Iasi, Romania
| | - Camelia Margareta Bogdanici
- Ophthalmology Department, Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa” Iasi, University Street No 16, 700115 Iasi, Romania
| | - Nicoleta Anton
- Ophthalmology Department, Faculty of Medicine, University of Medicine and Pharmacy “Gr. T. Popa” Iasi, University Street No 16, 700115 Iasi, Romania
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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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Alexopoulos P, Madu C, Wollstein G, Schuman JS. The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques. Front Med (Lausanne) 2022; 9:891369. [PMID: 35847772 PMCID: PMC9279625 DOI: 10.3389/fmed.2022.891369] [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] [Received: 03/07/2022] [Accepted: 05/23/2022] [Indexed: 11/22/2022] Open
Abstract
The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.
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Affiliation(s)
- Palaiologos Alexopoulos
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Chisom Madu
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
| | - Joel S. Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
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15
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Berenguer-Vidal R, Verdú-Monedero R, Morales-Sánchez J, Sellés-Navarro I, Kovalyk O, Sancho-Gómez JL. Decision Trees for Glaucoma Screening Based on the Asymmetry of the Retinal Nerve Fiber Layer in Optical Coherence Tomography. SENSORS 2022; 22:s22134842. [PMID: 35808338 PMCID: PMC9269200 DOI: 10.3390/s22134842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 12/10/2022]
Abstract
Purpose: The aim of this study was to analyze the relevance of asymmetry features between both eyes of the same patient for glaucoma screening using optical coherence tomography. Methods: Spectral-domain optical coherence tomography was used to estimate the thickness of the peripapillary retinal nerve fiber layer in both eyes of the patients in the study. These measurements were collected in a dataset from healthy and glaucoma patients. Several metrics for asymmetry in the retinal nerve fiber layer thickness between the two eyes were then proposed. These metrics were evaluated using the dataset by performing a statistical analysis to assess their significance as relevant features in the diagnosis of glaucoma. Finally, the usefulness of these asymmetry features was demonstrated by designing supervised machine learning models that can be used for the early diagnosis of glaucoma. Results: Machine learning models were designed and optimized, specifically decision trees, based on the values of proposed asymmetry metrics. The use of these models on the dataset provided good classification of the patients (accuracy 88%, sensitivity 70%, specificity 93% and precision 75%). Conclusions: The obtained machine learning models based on retinal nerve fiber layer asymmetry are simple but effective methods which offer a good trade-off in classification of patients and simplicity. The fast binary classification relies on a few asymmetry values of the retinal nerve fiber layer thickness, allowing their use in the daily clinical practice for glaucoma screening.
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Affiliation(s)
- Rafael Berenguer-Vidal
- Departamento de Ciencias Politécnicas, Universidad Católica de Murcia UCAM, 30107 Guadalupe, Spain;
| | - Rafael Verdú-Monedero
- Departamento de Tecnologías de la Información y Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (J.M.-S.); (O.K.); (J.-L.S.-G.)
- Correspondence:
| | - Juan Morales-Sánchez
- Departamento de Tecnologías de la Información y Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (J.M.-S.); (O.K.); (J.-L.S.-G.)
| | | | - Oleksandr Kovalyk
- Departamento de Tecnologías de la Información y Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (J.M.-S.); (O.K.); (J.-L.S.-G.)
| | - José-Luis Sancho-Gómez
- Departamento de Tecnologías de la Información y Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (J.M.-S.); (O.K.); (J.-L.S.-G.)
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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:jcm11133661. [PMID: 35806944 PMCID: PMC9267784 DOI: 10.3390/jcm11133661] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [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
- Correspondence: ; Tel.: +886-2-2905-2973
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Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis. J Pers Med 2022; 12:jpm12050756. [PMID: 35629177 PMCID: PMC9146635 DOI: 10.3390/jpm12050756] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/29/2022] [Accepted: 05/03/2022] [Indexed: 02/06/2023] Open
Abstract
Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy trial database. One traditional prediction method, logistic regression (LGR), and four ML techniques—naive Bayes, random forest (RF), classification and regression tree, and extreme gradient boosting (XGBoost)—were combined to construct our scheme. Area under the receiver operating characteristic curve (AUC) of RF (0.780) and XGBoost (0.717) was higher than that of LGR (0.674) in predicting vascular events. In predicting bleeding, AUC of RF (0.684) and XGBoost (0.618) showed higher values than those generated by LGR (0.605). Our integrated ML feature selection scheme based on the two convincing prediction techniques identified age, history of congestive heart failure and myocardial infarction, smoking, kidney function, and body mass index as major variables of vascular events; age, kidney function, smoking, bleeding history, concomitant use of specific drugs, and dabigatran dosage as major variables of bleeding. ML is an effective data analysis algorithm for solving complex medical data. Our results may provide preliminary direction for precision medicine.
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Exploratory Study of Palliative Care Utilization and Medical Expense for Inpatients at the End-of-Life. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19074263. [PMID: 35409941 PMCID: PMC8998871 DOI: 10.3390/ijerph19074263] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 02/04/2023]
Abstract
Background: Previous research mostly analyzed the utilization of palliative care for patients with cancer, and data regarding non-cancer inpatients are limited. Objectives: This research aimed to investigate the current situation regarding palliative care and the important factors that influence its utilization by inpatients (including inpatients with and without cancer) at the end of their lives. We also explored the feasibility of establishing a prediction model of palliative care utilization for inpatients at the end of their lives. These findings will allow medical staff to monitor and focus on those who may require palliative care, resulting in more end-of-life patients receiving palliative care and thereby reducing medical expense and improving their quality of life. Methods: This was a retrospective study based on real-world health information system (HIS) data from 5 different branches of Taipei City Hospital between 1 January 2018 and 31 December 2018 that enrolled a total of 1668 deceased inpatients. To explore palliative care utilization at the end of life, we used 5-fold cross-validation in four different statistical models to obtain the performance of predictive accuracy: logistic regression (LGR), classification and regression tree (CART), multivariate adaptive regression spline (MARS), and gradient boosting (GB). The important variables that may affect palliative care utilization by inpatients were also identified. Results: The results were as follows: (1) 497 (29.8%) inpatients received palliative care; (2) the average daily hospitalization cost of patients with cancer who received palliative care (NTD 5789 vs. NTD 12,115; p ≤ 0.001) and all patients who received palliative care (NTD 91,527 vs. NTD 186,981; p = 0.0037) were statistically significantly lower than patients who did not receive palliative care; (3) diagnosis, hospital, and length of stay (LOS) may affect palliative care utilization of inpatient; diagnosis, hospitalization unit, and length of hospitalization were statistically significant by LGR; (4) 51.5% of patients utilized palliative consultation services, and 48.5% utilized palliative care units; and (5) MARS had the most consistent results; its accuracy was 0.751, and the main predictors of palliative care utilization are hospital, medical expense, LOS, diagnosis, and Palliative Care Screening Tool-Taiwan version (TW-PCST) scores. Conclusions: The results reveal that palliative care utilization by inpatients remains low, and it is necessary to educate patients without cancer of the benefits and advantages of palliative care. Although data were limited, the predictability of the MARS model was 0.751; a better prediction model with more data is necessary for further research. Precisely predicting the need for palliative care may encourage patients and their family members to consider palliative care, which may balance both physical and mental care. Therefore, unnecessary medical care can be avoided and limited medical resources can be allocated to more patients in need.
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Su K, Wu J, Gu D, Yang S, Deng S, Khakimova AK. An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios. Diagnostics (Basel) 2021; 11:2288. [PMID: 34943525 PMCID: PMC8700766 DOI: 10.3390/diagnostics11122288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 12/04/2021] [Accepted: 12/06/2021] [Indexed: 12/19/2022] Open
Abstract
Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis.
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Affiliation(s)
- Kaixiang Su
- School of Management, Hefei University of Technology, Hefei 230009, China; (K.S.); (S.Y.)
| | - Jiao Wu
- School of Business, Northern Illinois University, DeKalb, IL 60115, USA;
| | - Dongxiao Gu
- School of Management, Hefei University of Technology, Hefei 230009, China; (K.S.); (S.Y.)
- Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei 230009, China
| | - Shanlin Yang
- School of Management, Hefei University of Technology, Hefei 230009, China; (K.S.); (S.Y.)
- Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei 230009, China
| | | | - Aida K. Khakimova
- Scientific-Research Center for Physical-Technical Informatics, Russian New University, Radio St., 22, 105005 Moscow, Russia;
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