1
|
Ardila CM, González-Arroyave D, Zuluaga-Gómez M. Predicting intensive care unit-acquired weakness: A multilayer perceptron neural network approach. World J Clin Cases 2024; 12:2023-2030. [PMID: 38680255 PMCID: PMC11045505 DOI: 10.12998/wjcc.v12.i12.2023] [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: 02/23/2024] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/16/2024] Open
Abstract
In this editorial, we comment on the article by Wang and Long, published in a recent issue of the World Journal of Clinical Cases. The article addresses the challenge of predicting intensive care unit-acquired weakness (ICUAW), a neuromuscular disorder affecting critically ill patients, by employing a novel processing strategy based on repeated machine learning. The editorial presents a dataset comprising clinical, demographic, and laboratory variables from intensive care unit (ICU) patients and employs a multilayer perceptron neural network model to predict ICUAW. The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW. This editorial contributes to the growing body of literature on predictive modeling in critical care, offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.
Collapse
Affiliation(s)
| | | | - Mateo Zuluaga-Gómez
- Department of Emergency, Universidad Pontificia Bolivariana, Medellín 0057, Colombia
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
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.
Collapse
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;
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Huang HH, Lu CJ, Jhou MJ, Liu TC, Yang CT, Hsieh SJ, Yang WJ, Chang HC, Chen MS. Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population. Risk Manag Healthc Policy 2023; 16:2469-2478. [PMID: 38024496 PMCID: PMC10658962 DOI: 10.2147/rmhp.s433193] [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: 07/31/2023] [Accepted: 11/01/2023] [Indexed: 12/01/2023] Open
Abstract
Purpose Approximately 20% of couples face infertility challenges and struggle to conceive naturally. Despite advances in artificial reproduction, its success hinges on sperm quality. Our previous study used five machine learning (ML) algorithms, random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting, to model health data from 1375 Taiwanese males and identified ten risk factors affecting sperm count. Methods We employed the CART algorithm to generate decision trees using identified risk factors to predict healthy sperm counts. Four error metrics, SMAPE, RAE, RRSE, and RMSE, were used to evaluate the decision trees. We identified the top five decision trees based on their low errors and discussed in detail the tree with the least error. Results The decision tree featuring the least error, comprising BMI, UA, ST, T-Cho/HDL-C ratio, and BUN, corroborated the negative impacts of metabolic syndrome, particularly high BMI, on sperm count, while emphasizing the link between good sleep and male fertility. Our study also sheds light on the potentially significant influence of high BUN on spermatogenesis. Two novel risk factors, T-Cho/HDL-C and UA, warrant further investigation. Conclusion The ML algorithm established a predictive model for healthcare personnel to assess low sperm counts. Refinement of the model using additional data is crucial for improved precision. The risk factors identified offer avenues for future investigations.
Collapse
Affiliation(s)
- Hung-Hsiang Huang
- Department of Urology, Surgery, Far Eastern Memorial Hospital, New Taipei City, 220, Taiwan
| | - Chi-Jie Lu
- 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
- 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
| | - Tzu-Chi Liu
- 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
| | - Shang-Ju Hsieh
- Department of Urology, Surgery, Far Eastern Memorial Hospital, New Taipei City, 220, Taiwan
| | - Wen-Jen Yang
- Health Screening Center, Chi Hsin Clinic, Taipei City, 104, Taiwan
| | - Hsiao-Chun Chang
- Department of Urology, Surgery, Far Eastern Memorial Hospital, New Taipei City, 220, Taiwan
| | - Ming-Shu Chen
- Department of Healthcare Administration, Asia Eastern University of Science and Technology, New Taipei City, 220, Taiwan
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Kim KM, Kim JH, Rhee HS, Youn BY. Development of a prediction model for the depression level of the elderly in low-income households: using decision trees, logistic regression, neural networks, and random forest. Sci Rep 2023; 13:11473. [PMID: 37455290 DOI: 10.1038/s41598-023-38742-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 07/13/2023] [Indexed: 07/18/2023] Open
Abstract
Korea is showing the fastest trend in the world in population aging; there is a high interest in the elderly population nationwide. Among the common chronic diseases, the elderly tends to have a high incidence of depression. That said, it has been vital to focus on preventing depression in the elderly in advance. Hence, this study aims to select the factors related to depression in low-income seniors identified in previous studies and to develop a prediction model. In this study, 2975 elderly people from low-income families were extracted using the 13th-year data of the Korea Welfare Panel Study (2018). Decision trees, logistic regression, neural networks, and random forest were applied to develop a predictive model among the numerous data mining techniques. In addition, the wrapper's stepwise backward elimination, which finds the optimal model by removing the least relevant factors, was applied. The evaluation of the model was confirmed via accuracy. It was verified that the final prediction model, in the case of a decision tree, showed the highest predictive power with an accuracy of 97.3%. Second, psychological factors, leisure life satisfaction, social support, subjective health awareness, and family support ranked higher than demographic factors influencing depression. Based on the results, an approach focused on psychological support is much needed to manage depression in low-income seniors. As predicting depression in the elderly varies on numerous influencing factors, using a decision tree may be beneficial to establish a firm prediction model to identify vital factors causing depression in the elderly population.
Collapse
Affiliation(s)
- Kyu-Min Kim
- Department of Health Policy and Management, Graduate School, Korea University, Seoul, Korea
- BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul, Korea
| | - Jae-Hak Kim
- Department of Health Policy and Management, Graduate School, Korea University, Seoul, Korea
- Department of Fitness Promotion and Rehabilitation Exercise, National Rehabilitation Center, Seoul, Korea
| | - Hyun-Sill Rhee
- Department of Health Policy and Management, College of Public Health Science, Korea University, Seoul, Korea
| | - Bo-Young Youn
- Department of Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul, Korea.
| |
Collapse
|
10
|
Cascella M, Montomoli J, Bellini V, Vittori A, Biancuzzi H, Dal Mas F, Bignami EG. Crossing the AI Chasm in Neurocritical Care. COMPUTERS 2023; 12:83. [DOI: 10.3390/computers12040083] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Despite the growing interest in possible applications of computer science and artificial intelligence (AI) in the field of neurocritical care (neuro-ICU), widespread clinical applications are still missing. In neuro-ICU, the collection and analysis in real time of large datasets can play a crucial role in advancing this medical field and improving personalized patient care. For example, AI algorithms can detect subtle changes in brain activity or vital signs, alerting clinicians to potentially life-threatening conditions and facilitating rapid intervention. Consequently, data-driven AI and predictive analytics can greatly enhance medical decision making, diagnosis, and treatment, ultimately leading to better outcomes for patients. Nevertheless, there is a significant disparity between the current capabilities of AI systems and the potential benefits and applications that could be achieved with more advanced AI technologies. This gap is usually indicated as the AI chasm. In this paper, the underlying causes of the AI chasm in neuro-ICU are analyzed, along with proposed recommendations for utilizing AI to attain a competitive edge, foster innovation, and enhance patient outcomes. To bridge the AI divide in neurocritical care, it is crucial to foster collaboration among researchers, clinicians, and policymakers, with a focus on specific use cases. Additionally, strategic investments in AI technology, education and training, and infrastructure are needed to unlock the potential of AI technology. Before implementing a technology in patient care, it is essential to conduct thorough studies and establish clinical validation in real-world environments to ensure its effectiveness and safety. Finally, the development of ethical and regulatory frameworks is mandatory to ensure the secure and efficient deployment of AI technology throughout the process.
Collapse
Affiliation(s)
- Marco Cascella
- Pain Unit and Research, Istituto Nazionale Tumori IRCCS Fondazione Pascale, 80100 Napoli, Italy
| | - Jonathan Montomoli
- Department of Anesthesia and Intensive Care, Infermi Hospital, AUSL Romagna, 47923 Rimini, Italy
| | - Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| | - Alessandro Vittori
- Department of Anesthesia and Critical Care, ARCO ROMA, Ospedale Pediatrico Bambino Gesù, IRCCS, 00165 Rome, Italy
| | - Helena Biancuzzi
- Department of Economics, Ca’ Foscari University, 30121 Venice, Italy
| | - Francesca Dal Mas
- Department of Management, Ca’ Foscari University, 30121 Venice, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques. Healthcare (Basel) 2022; 10:healthcare10122496. [PMID: 36554020 PMCID: PMC9778302 DOI: 10.3390/healthcare10122496] [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: 11/01/2022] [Revised: 11/28/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
With the rapid development of medicine and technology, machine learning (ML) techniques are extensively applied to medical informatics and the suboptimal health field to identify critical predictor variables and risk factors. Metabolic syndrome (MetS) and chronic kidney disease (CKD) are important risk factors for many comorbidities and complications. Existing studies that utilize different statistical or ML algorithms to perform CKD data analysis mostly analyze the early-stage subjects directly, but few studies have discussed the predictive models and important risk factors for the stage-III CKD high-risk health screening population. The middle stages 3a and 3b of CKD indicate moderate renal failure. This study aims to construct an effective hybrid important risk factor evaluation scheme for subjects with MetS and CKD stages III based on ML predictive models. The six well-known ML techniques, namely random forest (RF), logistic regression (LGR), multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost), and a light gradient boosting machine (LightGBM), were used in the proposed scheme. The data were sourced from the Taiwan health examination indicators and the questionnaire responses of 71,108 members between 2005 and 2017. In total, 375 stage 3a CKD and 50 CKD stage 3b CKD patients were enrolled, and 33 different variables were used to evaluate potential risk factors. Based on the results, the top five important variables, namely BUN, SBP, Right Intraocular Pressure (R-IOP), RBCs, and T-Cho/HDL-C (C/H), were identified as significant variables for evaluating the subjects with MetS and CKD stage 3a or 3b.
Collapse
|
13
|
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.
Collapse
|
14
|
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.
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|