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Spoladore D, Stella F, Tosi M, Lorenzini EC, Bettini C. A knowledge-based decision support system to support family doctors in personalizing type-2 diabetes mellitus medical nutrition therapy. Comput Biol Med 2024; 180:109001. [PMID: 39126791 DOI: 10.1016/j.compbiomed.2024.109001] [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: 05/07/2024] [Revised: 07/12/2024] [Accepted: 08/05/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND Type-2 Diabetes Mellitus (T2D) is a growing concern worldwide, and family doctors are called to help diabetic patients manage this chronic disease, also with Medical Nutrition Therapy (MNT). However, MNT for Diabetes is usually standardized, while it would be much more effective if tailored to the patient. There is a gap in patient-tailored MNT which, if addressed, could support family doctors in delivering effective recommendations. In this context, decision support systems (DSSs) are valuable tools for physicians to support MNT for T2D patients - as long as DSSs are transparent to humans in their decision-making process. Indeed, the lack of transparency in data-driven DSS might hinder their adoption in clinical practice, thus leaving family physicians to adopt general nutrition guidelines provided by the national healthcare systems. METHOD This work presents a prototypical ontology-based clinical Decision Support System (OnT2D- DSS) aimed at assisting general practice doctors in managing T2D patients, specifically in creating a tailored dietary plan, leveraging clinical expert knowledge. OnT2D-DSS exploits clinical expert knowledge formalized as a domain ontology to identify a patient's phenotype and potential comorbidities, providing personalized MNT recommendations for macro- and micro-nutrient intake. The system can be accessed via a prototypical interface. RESULTS Two preliminary experiments are conducted to assess both the quality and correctness of the inferences provided by the system and the usability and acceptance of the OnT2D-DSS (conducted with nutrition experts and family doctors, respectively). CONCLUSIONS Overall, the system is deemed accurate by the nutrition experts and valuable by the family doctors, with minor suggestions for future improvements collected during the experiments.
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Affiliation(s)
- Daniele Spoladore
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council (Cnr), Lecco, Italy.
| | - Francesco Stella
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council (Cnr), Lecco, Italy; Department of Computer Science, University of Milan, Milan, Italy.
| | - Martina Tosi
- Department of Health Sciences, University of Milan, Milan, Italy.
| | | | - Claudio Bettini
- Department of Computer Science, University of Milan, Milan, Italy.
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Morgan-Benita JA, Celaya-Padilla JM, Luna-García H, Galván-Tejada CE, Cruz M, Galván-Tejada JI, Gamboa-Rosales H, Sánchez-Reyna AG, Rondon D, Villalba-Condori KO. Setting Ranges in Potential Biomarkers for Type 2 Diabetes Mellitus Patients Early Detection By Sex-An Approach with Machine Learning Algorithms. Diagnostics (Basel) 2024; 14:1623. [PMID: 39125499 PMCID: PMC11311857 DOI: 10.3390/diagnostics14151623] [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: 06/19/2024] [Revised: 07/20/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
Type 2 diabetes mellitus (T2DM) is one of the most common metabolic diseases in the world and poses a significant public health challenge. Early detection and management of this metabolic disorder is crucial to prevent complications and improve outcomes. This paper aims to find core differences in male and female markers to detect T2DM by their clinic and anthropometric features, seeking out ranges in potential biomarkers identified to provide useful information as a pre-diagnostic tool whie excluding glucose-related biomarkers using machine learning (ML) models. We used a dataset containing clinical and anthropometric variables from patients diagnosed with T2DM and patients without TD2M as control. We applied feature selection with three different techniques to identify relevant biomarker models: an improved recursive feature elimination (RFE) evaluating each set from all the features to one feature with the Akaike information criterion (AIC) to find optimal outputs; Least Absolute Shrinkage and Selection Operator (LASSO) with glmnet; and Genetic Algorithms (GA) with GALGO and forward selection (FS) applied to GALGO output. We then used these for comparison with the AIC to measure the performance of each technique and collect the optimal set of global features. Then, an implementation and comparison of five different ML models was carried out to identify the most accurate and interpretable one, considering the following models: logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), and nearest centroid (Nearcent). The models were then combined in an ensemble to provide a more robust approximation. The results showed that potential biomarkers such as systolic blood pressure (SBP) and triglycerides are together significantly associated with T2DM. This approach also identified triglycerides, cholesterol, and diastolic blood pressure as biomarkers with differences between male and female actors that have not been previously reported in the literature. The most accurate ML model was selection with RFE and random forest (RF) as the estimator improved with the AIC, which achieved an accuracy of 0.8820. In conclusion, this study demonstrates the potential of ML models in identifying potential biomarkers for early detection of T2DM, excluding glucose-related biomarkers as well as differences between male and female anthropometric and clinic profiles. These findings may help to improve early detection and management of the T2DM by accounting for differences between male and female subjects in terms of anthropometric and clinic profiles, potentially reducing healthcare costs and improving personalized patient attention. Further research is needed to validate these potential biomarkers ranges in other populations and clinical settings.
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Affiliation(s)
- Jorge A. Morgan-Benita
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Zacatecas 98000, Mexico; (J.A.M.-B.); (H.L.-G.); (C.E.G.-T.); (J.I.G.-T.); (H.G.-R.); (A.G.S.-R.)
| | - José M. Celaya-Padilla
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Zacatecas 98000, Mexico; (J.A.M.-B.); (H.L.-G.); (C.E.G.-T.); (J.I.G.-T.); (H.G.-R.); (A.G.S.-R.)
| | - Huizilopoztli Luna-García
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Zacatecas 98000, Mexico; (J.A.M.-B.); (H.L.-G.); (C.E.G.-T.); (J.I.G.-T.); (H.G.-R.); (A.G.S.-R.)
| | - Carlos E. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Zacatecas 98000, Mexico; (J.A.M.-B.); (H.L.-G.); (C.E.G.-T.); (J.I.G.-T.); (H.G.-R.); (A.G.S.-R.)
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Ciudad de México 06720, Mexico;
| | - Jorge I. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Zacatecas 98000, Mexico; (J.A.M.-B.); (H.L.-G.); (C.E.G.-T.); (J.I.G.-T.); (H.G.-R.); (A.G.S.-R.)
| | - Hamurabi Gamboa-Rosales
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Zacatecas 98000, Mexico; (J.A.M.-B.); (H.L.-G.); (C.E.G.-T.); (J.I.G.-T.); (H.G.-R.); (A.G.S.-R.)
| | - Ana G. Sánchez-Reyna
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Zacatecas 98000, Mexico; (J.A.M.-B.); (H.L.-G.); (C.E.G.-T.); (J.I.G.-T.); (H.G.-R.); (A.G.S.-R.)
| | - David Rondon
- Departamento de Estudios Generales, Universidad Continental, Arequipa 04001, Peru;
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Chen L, Shao X, Yu P. Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis. Endocrine 2024; 84:890-902. [PMID: 38141061 DOI: 10.1007/s12020-023-03637-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND Machine learning is increasingly recognized as a viable approach for identifying risk factors associated with diabetic kidney disease (DKD). However, the current state of real-world research lacks a comprehensive systematic analysis of the predictive performance of machine learning (ML) models for DKD. OBJECTIVES The objectives of this study were to systematically summarize the predictive capabilities of various ML methods in forecasting the onset and the advancement of DKD, and to provide a basic outline for ML methods in DKD. METHODS We have searched mainstream databases, including PubMed, Web of Science, Embase, and MEDLINE databases to obtain the eligible studies. Subsequently, we categorized various ML techniques and analyzed the differences in their performance in predicting DKD. RESULTS Logistic regression (LR) was the prevailing ML method, yielding an overall pooled area under the receiver operating characteristic curve (AUROC) of 0.83. On the other hand, the non-LR models also performed well with an overall pooled AUROC of 0.80. Our t-tests showed no statistically significant difference in predicting ability between LR and non-LR models (t = 1.6767, p > 0.05). CONCLUSION All ML predicting models yielded relatively satisfied DKD predicting ability with their AUROCs greater than 0.7. However, we found no evidence that non-LR models outperformed the LR model. LR exhibits high performance or accuracy in practice, while it is known for algorithmic simplicity and computational efficiency compared to others. Thus, LR may be considered a cost-effective ML model in practice.
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Affiliation(s)
- Lianqin Chen
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
| | - Xian Shao
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
| | - Pei Yu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China.
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Kanbour S, Harris C, Lalani B, Wolf RM, Fitipaldi H, Gomez MF, Mathioudakis N. Machine Learning Models for Prediction of Diabetic Microvascular Complications. J Diabetes Sci Technol 2024; 18:273-286. [PMID: 38189280 PMCID: PMC10973856 DOI: 10.1177/19322968231223726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
IMPORTANCE AND AIMS Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN). METHODS A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics. RESULTS Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance. CONCLUSIONS AND RELEVANCE There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.
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Affiliation(s)
| | - Catharine Harris
- Division of Endocrinology, Diabetes,
& Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Benjamin Lalani
- Division of Endocrinology, Diabetes,
& Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Risa M. Wolf
- Division of Pediatric Endocrinology,
Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hugo Fitipaldi
- Department of Clinical Sciences, Lund
University Diabetes Centre, Lund University, Malmö, Sweden
| | - Maria F. Gomez
- Department of Clinical Sciences, Lund
University Diabetes Centre, Lund University, Malmö, Sweden
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes,
& Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
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Yang TH, Chen YF, Cheng YF, Huang JN, Wu CS, Chu YC. Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S. BioData Min 2023; 16:35. [PMID: 38098102 PMCID: PMC10722728 DOI: 10.1186/s13040-023-00351-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023] Open
Abstract
OBJECTIVES The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. METHODS We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model-which had the greatest AUC of 0.83 (95% CI 0.81-0.85)-was then only used on the holdout testing data. RESULTS On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79-0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques-specifically, the LGBM classifier-with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. CONCLUSIONS Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.
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Affiliation(s)
- Tzong-Hann Yang
- Department of Otorhinolaryngology, Taipei City Hospital, Taipei, 100, Taiwan
- General Education Center, University of Taipei, Taipei, 10671, Taiwan
- Department of Speech-Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, 112303, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Fu Chen
- Department of Speech-Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, 112303, Taiwan
| | - Yen-Fu Cheng
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, 112, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, Taipei, 112, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Jue-Ni Huang
- Information Management Office, Taipei Veterans General Hospital, Taipei, 112, Taiwan
| | - Chuan-Song Wu
- Department of Otorhinolaryngology, Taipei City Hospital, Taipei, 100, Taiwan.
- College of Science and Engineering, Fu Jen University, Taipei, 243, Taiwan.
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, Taipei, 112, Taiwan.
- Big Data Center, Taipei Veterans General Hospital, Taipei, 112, Taiwan.
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, 112, Taiwan.
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Su X, Lin S, Huang Y. Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy. Sci Rep 2023; 13:20427. [PMID: 37993534 PMCID: PMC10665410 DOI: 10.1038/s41598-023-47449-2] [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: 09/22/2023] [Accepted: 11/14/2023] [Indexed: 11/24/2023] Open
Abstract
Despite efforts to diagnose diabetic nephropathy (DN) using biochemical data or ultrasound imaging separately, a significant gap exists regarding the development of integrated models combining both modalities for enhanced early DN diagnosis. Therefore, we aimed to assess the ability of machine learning models containing two-dimensional ultrasound imaging and biochemical data to diagnose early DN in patients with type 2 diabetes mellitus (T2DM). This retrospective study included 219 patients, divided into a training or test group at an 8:2 ratio. Features were selected using minimum redundancy maximum relevance and random forest-recursive feature elimination. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) for sensitivity, specificity, Matthews Correlation Coefficient, F1 score, and accuracy. K-nearest neighbor, support vector machine, and logistic regression models could diagnose early DN, with AUC values of 0.94, 0.85, and 0.85 in the training cohort and 0.91, 0.84, and 0.84 in the test cohort, respectively. Early DN diagnosing using two-dimensional ultrasound-based radiomics models can potentially revolutionize T2DM patient care by enabling proactive interventions, ultimately improving patient outcomes. Our integrated approach showcases the power of artificial intelligence in medical imaging, enhancing early disease detection strategies with far-reaching applications across medical disciplines.
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Affiliation(s)
- Xuee Su
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
- Department of Anaesthesia, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Shu Lin
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China.
- Diabetes and Metabolism Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney, NSW, 2010, Australia.
| | - Yinqiong Huang
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China.
- Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China.
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Tang SY, Chen TH, Kuo KL, Huang JN, Kuo CT, Chu YC. Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study. J Chin Med Assoc 2023; 86:1020-1027. [PMID: 37713313 DOI: 10.1097/jcma.0000000000000994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients. METHODS A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost), light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts. RESULTS Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptor-binding domain antibodies. CONCLUSION This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.
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Affiliation(s)
- Shao-Yu Tang
- Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC
| | - Tz-Heng Chen
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Institute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Ko-Lin Kuo
- Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC
| | - Jue-Ni Huang
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Chen-Tsung Kuo
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Big Data Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan, ROC
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Big Data Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan, ROC
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Tao X, Jiang M, Liu Y, Hu Q, Zhu B, Hu J, Guo W, Wu X, Xiong Y, Shi X, Zhang X, Han X, Li W, Tong R, Long E. Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms. Sci Rep 2023; 13:16437. [PMID: 37777593 PMCID: PMC10543442 DOI: 10.1038/s41598-023-43240-5] [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: 07/18/2023] [Accepted: 09/21/2023] [Indexed: 10/02/2023] Open
Abstract
Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020. After data preprocessing, data inputting, data sampling, and feature screening, 16 kinds of machine learning methods were used to construct prediction models, and 5 prediction models with the best prediction performance were screened respectively. A total of 100,000 cases were included to establish the FBG model, and 2,169 cases were established to establish the HbA1c model. The best prediction model both of FBG and HbA1c finally obtained are realized by ensemble learning and modified random forest inputting, the AUC values are 0.819 and 0.970, respectively. The most important indicators of the FBG and HbA1c prediction model were FBG and HbA1c. Medication compliance, follow-up outcome, dietary habits, BMI, and waist circumference also had a greater impact on FBG levels. The prediction accuracy of the models of the two blood glucose control indicators is high and has certain clinical applicability.HbA1c and FBG are mutually important predictors, and there is a close relationship between them.
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Affiliation(s)
- Xue Tao
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Min Jiang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610044, Sichuan, China
| | - Yumeng Liu
- Department of Pharmacy, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Qi Hu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, Sichuan, China
| | - Baoqiang Zhu
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Jiaqiang Hu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Wenmei Guo
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Xingwei Wu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Yu Xiong
- Institute of Materia Medica, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, 100050, China
| | - Xia Shi
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Xueli Zhang
- Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China
| | - Xu Han
- Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China
| | - Wenyuan Li
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Rongsheng Tong
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Enwu Long
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China.
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Liao CM, Su CT, Huang HC, Lin CM. Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression. Biomedicines 2023; 11:1664. [PMID: 37371759 DOI: 10.3390/biomedicines11061664] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Kidney diseases can cause severe morbidity, mortality, and health burden. Determining the risk factors associated with kidney damage and deterioration has become a priority for the prevention and treatment of kidney disease. This study followed 497 patients with stage 3-5 chronic kidney disease (CKD) who were treated at the ward of Taipei Veterans General Hospital from January 2006 to 2019 in Taiwan. The patients underwent 3-year-long follow-up sessions for clinical measurements, which occurred every 3 months. Three time-dependent survival models, namely the Cox proportional hazard model (Cox PHM), random survival forest (RSF), and an artificial neural network (ANN), were used to process patient demographics and laboratory data for predicting progression to renal failure, and important features for optimal prediction were evaluated. The individual prediction of CKD progression was validated using the Kaplan-Meier estimation method, based on patients' true outcomes during and beyond the study period. The results showed that the average concordance indexes for the cross-validation of the Cox PHM, ANN, and RSF models were 0.71, 0.72, and 0.89, respectively. RSF had the best predictive performances for CKD patients within the 3 years of follow-up sessions, with a sensitivity of 0.79 and specificity of 0.88. Creatinine, age, estimated glomerular filtration rate, and urine protein to creatinine ratio were useful factors for predicting the progression of CKD patients in the RSF model. These results may be helpful for instantaneous risk prediction at each follow-up session for CKD patients.
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Affiliation(s)
- Chen-Mao Liao
- Department of Applied Statistics and Information Science, Ming Chuan University, Taoyuan 333, Taiwan
| | - Chuan-Tsung Su
- Department of Healthcare Information and Management, Ming Chuan University, Taoyuan 333, Taiwan
| | - Hao-Che Huang
- Department of Applied Statistics and Information Science, Ming Chuan University, Taoyuan 333, Taiwan
| | - Chih-Ming Lin
- Department of Healthcare Information and Management, Ming Chuan University, Taoyuan 333, Taiwan
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