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Sheng B, Pushpanathan K, Guan Z, Lim QH, Lim ZW, Yew SME, Goh JHL, Bee YM, Sabanayagam C, Sevdalis N, Lim CC, Lim CT, Shaw J, Jia W, Ekinci EI, Simó R, Lim LL, Li H, Tham YC. Artificial intelligence for diabetes care: current and future prospects. Lancet Diabetes Endocrinol 2024; 12:569-595. [PMID: 39054035 DOI: 10.1016/s2213-8587(24)00154-2] [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] [Received: 02/02/2024] [Revised: 03/28/2024] [Accepted: 05/16/2024] [Indexed: 07/27/2024]
Abstract
Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.
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Affiliation(s)
- Bin Sheng
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China; Key Laboratory of Artificial Intelligence, Ministry of Education, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Krithi Pushpanathan
- Centre of Innovation and Precision Eye Health, Department of Ophthalmology, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Zhouyu Guan
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Quan Hziung Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Zhi Wei Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Samantha Min Er Yew
- Centre of Innovation and Precision Eye Health, Department of Ophthalmology, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Yong Mong Bee
- Department of Endocrinology, Singapore General Hospital, Singapore; SingHealth Duke-National University of Singapore Diabetes Centre, Singapore Health Services, Singapore
| | - Charumathi Sabanayagam
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Nick Sevdalis
- Centre for Behavioural and Implementation Science Interventions, National University of Singapore, Singapore
| | | | - Chwee Teck Lim
- Department of Biomedical Engineering, National University of Singapore, Singapore; Institute for Health Innovation and Technology, National University of Singapore, Singapore; Mechanobiology Institute, National University of Singapore, Singapore
| | - Jonathan Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Weiping Jia
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Elif Ilhan Ekinci
- Australian Centre for Accelerating Diabetes Innovations, Melbourne Medical School and Department of Medicine, University of Melbourne, Melbourne, VIC, Australia; Department of Endocrinology, Austin Health, Melbourne, VIC, Australia
| | - Rafael Simó
- Diabetes and Metabolism Research Unit, Vall d'Hebron University Hospital and Vall d'Hebron Research Institute, Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain
| | - Lee-Ling Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Asia Diabetes Foundation, Hong Kong Special Administrative Region, China
| | - Huating Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
| | - Yih-Chung Tham
- Centre of Innovation and Precision Eye Health, Department of Ophthalmology, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
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Nayak S, Amin A, Reghunath SR, Thunga G, Acharya U D, Shivashankara KN, Prabhu Attur R, Acharya LD. Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study. Int J Med Inform 2024; 190:105546. [PMID: 39003788 DOI: 10.1016/j.ijmedinf.2024.105546] [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: 04/30/2024] [Revised: 07/02/2024] [Accepted: 07/08/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial. OBJECTIVE To develop a prediction model to identify the stages of DKD and the factors contributing to progression to each stage using machine learning. METHODOLOGY A retrospective study was conducted in a South Indian tertiary care hospital and collected the details of patients diagnosed with DKD from January 2017 to January 2022. Bayesian optimization-based machine learning techniques such as classification and regression were employed. The model was developed with the help of an optimization framework that effectively balances classification, prediction accuracy, and explainability. RESULTS Of the 311 patients diagnosed with DKD, 227 were selected for the study. A system for predicting DKD has been created for a patient dataset utilizing a variety of machine-learning approaches. The eXtreme gradient (XG) Boost method excelled, achieving 88.75% accuracy, 88.57% precision, 91.4% sensitivity,100% specificity, and 89.49% F1-score. An interpretable data-driven method highlights significant features for early DKD diagnosis. The best explainable prediction model uses the XG Boost classifier, revealing serum uric acid, urea, phosphorous, red blood cells, calcium, and absolute eosinophil count as the major predictors influencing the progression of DKD. In the case of regression models, the gradient boost regressor performed the best, with an R2 score of 0.97. CONCLUSION Machine learning algorithms can effectively predict the stages of DKD and thus help physicians in providing patients with personalized care at the right time.
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Affiliation(s)
- Sandhya Nayak
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Ashwini Amin
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Swetha R Reghunath
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Girish Thunga
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Dinesh Acharya U
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - K N Shivashankara
- Department of General Medicine, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Ravindra Prabhu Attur
- Department of Nephrology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Leelavathi D Acharya
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
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Sammut-Powell C, Sisk R, Vazquez-Mendez E, Vasnawala H, Goncalves S, Edge M, Cameron R. Global Validation of a Model to Predict Reduced Estimated GFR in People With Type 2 Diabetes Without Diagnosis of CKD. Kidney Int Rep 2024; 9:2047-2055. [PMID: 39081746 PMCID: PMC11284396 DOI: 10.1016/j.ekir.2024.04.005] [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: 08/13/2023] [Revised: 03/31/2024] [Accepted: 04/01/2024] [Indexed: 08/02/2024] Open
Abstract
Introduction A minimal-resource model for predicting reduced kidney function among people with type 2 diabetes and no diagnosis of chronic kidney disease (CKD) stages 3 to 5 was previously developed in a UK population to pre-screen for undiagnosed CKD. This study aims to evaluate the performance of the model on a global population and assess its adequacy with and without regional adjustment. Methods A retrospective observational study was performed using data collected from the iCaReMe global registry (NCT03549754) and the DISCOVER study (NCT02322762 and NCT02226822). Patients were grouped by their World Health Organization classified region. An estimated glomerular filtration rate (eGFR) <60 ml/min per 1.73 m2 was the marker of reduced kidney function. A regional-intercept recalibration was applied to adjust for regional variation. Discrimination and calibration were evaluated for the UK-developed and recalibrated models. Results A total of 14,180 patients (46 countries, 6 regions) were identified with type 2 diabetes, no previous diagnosis of CKD stages 3 to 5, and had a serum creatinine measurement or eGFR recorded. The UK model underestimated risk when applied globally and was deemed inadequate. The model with regional adjustment achieved the target sensitivity (80.5%; 95% confidence interval [CI]: 78.8%-82.3%) and demonstrated a relative improvement of 51.5% (95% CI: 48.1%-55.1%) in the positive predictive value (PPV), compared to a screen-all approach. Conclusion The regional-adjusted model demonstrated adequate performance globally. Incorporating the model within practice could help clinicians to risk-stratify and prioritize patients at high risk. This could enable improved efficiency via risk-tailored screening, particularly in lower-middle-income countries (LMICs).
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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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Mesquita F, Bernardino J, Henriques J, Raposo JF, Ribeiro RT, Paredes S. Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review. J Diabetes Metab Disord 2024; 23:825-839. [PMID: 38932857 PMCID: PMC11196462 DOI: 10.1007/s40200-023-01357-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 11/20/2023] [Indexed: 06/28/2024]
Abstract
Purpose Diabetes is a major public health challenge with widespread prevalence, often leading to complications such as Diabetic Nephropathy (DN)-a chronic condition that progressively impairs kidney function. In this context, it is important to evaluate if Machine learning models can exploit the inherent temporal factor in clinical data to predict the risk of developing DN faster and more accurately than current clinical models. Methods Three different databases were used for this literature review: Scopus, Web of Science, and PubMed. Only articles written in English and published between January 2015 and December 2022 were included. Results We included 11 studies, from which we discuss a number of algorithms capable of extracting knowledge from clinical data, incorporating dynamic aspects in patient assessment, and exploring their evolution over time. We also present a comparison of the different approaches, their performance, advantages, disadvantages, interpretation, and the value that the time factor can bring to a more successful prediction of diabetic nephropathy. Conclusion Our analysis showed that some studies ignored the temporal factor, while others partially exploited it. Greater use of the temporal aspect inherent in Electronic Health Records (EHR) data, together with the integration of omics data, could lead to the development of more reliable and powerful predictive models.
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Affiliation(s)
- F. Mesquita
- Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes - Quinta da Nora, 3030-199 Coimbra, Portugal
| | - J. Bernardino
- Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes - Quinta da Nora, 3030-199 Coimbra, Portugal
- Center for Informatics and Systems of University of Coimbra, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal
| | - J. Henriques
- Center for Informatics and Systems of University of Coimbra, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal
| | - JF. Raposo
- Education and Research Center, APDP Diabetes Portugal, Rua Do Salitre 118-120, 1250-203 Lisbon, Portugal
| | - RT. Ribeiro
- Education and Research Center, APDP Diabetes Portugal, Rua Do Salitre 118-120, 1250-203 Lisbon, Portugal
| | - S. Paredes
- Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes - Quinta da Nora, 3030-199 Coimbra, Portugal
- Center for Informatics and Systems of University of Coimbra, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal
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Sammut-Powell C, Sisk R, Silva-Tinoco R, de la Pena G, Almeda-Valdes P, Juarez Comboni SC, Goncalves S, Cameron R. External validation of a minimal-resource model to predict reduced estimated glomerular filtration rate in people with type 2 diabetes without diagnosis of chronic kidney disease in Mexico: a comparison between country-level and regional performance. Front Endocrinol (Lausanne) 2024; 15:1253492. [PMID: 38586458 PMCID: PMC10998449 DOI: 10.3389/fendo.2024.1253492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 02/19/2024] [Indexed: 04/09/2024] Open
Abstract
Background Patients with type 2 diabetes are at an increased risk of chronic kidney disease (CKD) hence it is recommended that they receive annual CKD screening. The huge burden of diabetes in Mexico and limited screening resource mean that CKD screening is underperformed. Consequently, patients often have a late diagnosis of CKD. A regional minimal-resource model to support risk-tailored CKD screening in patients with type 2 diabetes has been developed and globally validated. However, population heath and care services between countries within a region are expected to differ. The aim of this study was to evaluate the performance of the model within Mexico and compare this with the performance demonstrated within the Americas in the global validation. Methods We performed a retrospective observational study with data from primary care (Clinic Specialized in Diabetes Management in Mexico City), tertiary care (Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán) and the Mexican national survey of health and nutrition (ENSANUT-MC 2016). We applied the minimal-resource model across the datasets and evaluated model performance metrics, with the primary interest in the sensitivity and increase in the positive predictive value (PPV) compared to a screen-everyone approach. Results The model was evaluated on 2510 patients from Mexico (primary care: 1358, tertiary care: 735, ENSANUT-MC: 417). Across the Mexico data, the sensitivity was 0.730 (95% CI: 0.689 - 0.779) and the relative increase in PPV was 61.0% (95% CI: 52.1% - 70.8%). These were not statistically different to the regional performance metrics for the Americas (sensitivity: p=0.964; relative improvement: p=0.132), however considerable variability was observed across the data sources. Conclusion The minimal-resource model performs consistently in a representative Mexican population sample compared with the Americas regional performance. In primary care settings where screening is underperformed and access to laboratory testing is limited, the model can act as a risk-tailored CKD screening solution, directing screening resources to patients who are at highest risk.
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Affiliation(s)
| | - Rose Sisk
- Gendius Ltd, Alderley Edge, United Kingdom
| | - Ruben Silva-Tinoco
- Clinic Specialized in the Diabetes Management of the Mexico City Government, Public Health Services of the Mexico City Government, Mexico, City, Mexico
| | - Gustavo de la Pena
- Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ), Mexico City, Mexico
| | - Paloma Almeda-Valdes
- Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ), Mexico City, Mexico
- Metabolic Diseases Research, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
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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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Sheng Y, Zhang C, Huang J, Wang D, Xiao Q, Zhang H, Ha X. Comparison of conventional mathematical model and machine learning model based on recent advances in mathematical models for predicting diabetic kidney disease. Digit Health 2024; 10:20552076241238093. [PMID: 38465295 PMCID: PMC10921860 DOI: 10.1177/20552076241238093] [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: 09/14/2023] [Accepted: 02/22/2024] [Indexed: 03/12/2024] Open
Abstract
Previous research suggests that mathematical models could serve as valuable tools for diagnosing or predicting diseases like diabetic kidney disease, which often necessitate invasive examinations for conclusive diagnosis. In the big-data era, there are several mathematical modeling methods, but generally, two types are recognized: conventional mathematical model and machine learning model. Each modeling method has its advantages and disadvantages, but a thorough comparison of the two models is lacking. In this article, we describe and briefly compare the conventional mathematical model and machine learning model, and provide research prospects in this field.
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Affiliation(s)
- Yingda Sheng
- Gansu University of Chinese Medicine, Lanzhou, Gansu, China
- The 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Lanzhou, Gansu, China
| | - Caimei Zhang
- Gansu University of Chinese Medicine, Lanzhou, Gansu, China
- The 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Lanzhou, Gansu, China
| | - Jing Huang
- Gansu University of Chinese Medicine, Lanzhou, Gansu, China
- The 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Lanzhou, Gansu, China
| | - Dan Wang
- Gansu University of Chinese Medicine, Lanzhou, Gansu, China
- The 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Lanzhou, Gansu, China
| | - Qian Xiao
- Gansu University of Chinese Medicine, Lanzhou, Gansu, China
- The 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Lanzhou, Gansu, China
| | - Haocheng Zhang
- The Second Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Xiaoqin Ha
- The 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Lanzhou, Gansu, China
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Shi S, Gao L, Zhang J, Zhang B, Xiao J, Xu W, Tian Y, Ni L, Wu X. The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients. BMC Med Inform Decis Mak 2023; 23:241. [PMID: 37904184 PMCID: PMC10617171 DOI: 10.1186/s12911-023-02343-9] [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: 06/14/2023] [Accepted: 10/16/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Diabetic kidney disease (DKD) has become the largest cause of end-stage kidney disease. Early and accurate detection of DKD is beneficial for patients. The present detection depends on the measurement of albuminuria or the estimated glomerular filtration rate, which is invasive and not optimal; therefore, new detection tools are urgently needed. Meanwhile, a close relationship between diabetic retinopathy and DKD has been reported; thus, we aimed to develop a novel detection algorithm for DKD using artificial intelligence technology based on retinal vascular parameters combined with several easily available clinical parameters in patients with type-2 diabetes. METHODS A total of 515 consecutive patients with type-2 diabetes mellitus from Xiangyang Central Hospital were included. Patients were stratified by DKD diagnosis and split randomly into either the training set (70%, N = 360) or the testing set (30%, N = 155) (random seed = 1). Data from the training set were used to develop the machine learning algorithm (MLA), while those from the testing set were used to validate the MLA. Model performances were evaluated. RESULTS The MLA using the random forest classifier presented optimal performance compared with other classifiers. When validated, the accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model were 84.5%(95% CI 83.3-85.7), 84.5%(82.3-86.7), 84.5%(82.7-86.3), 0.845(0.831-0.859), and 0.914(0.903-0.925), respectively. CONCLUSIONS A new machine learning algorithm for DKD diagnosis based on fundus images and 8 easily available clinical parameters was developed, which indicated that retinal vascular changes can assist in DKD screening and detection.
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Affiliation(s)
- Shaomin Shi
- Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China
| | - Ling Gao
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China
| | - Juan Zhang
- Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China
| | - Baifang Zhang
- Department of Biochemistry, Wuhan University TaiKang Medical School (School of Basic Medical Sciences), Wuhan, 430071, Hubei, China
| | - Jing Xiao
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China
| | - Wan Xu
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China
| | - Yuan Tian
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China.
| | - Lihua Ni
- Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China.
| | - Xiaoyan Wu
- Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China.
- Department of General Practice, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China.
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Iparraguirre-Villanueva O, Espinola-Linares K, Flores Castañeda RO, Cabanillas-Carbonell M. Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes. Diagnostics (Basel) 2023; 13:2383. [PMID: 37510127 PMCID: PMC10378239 DOI: 10.3390/diagnostics13142383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.
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11
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Liu XZ, Duan M, Huang HD, Zhang Y, Xiang TY, Niu WC, Zhou B, Wang HL, Zhang TT. Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study. Front Endocrinol (Lausanne) 2023; 14:1184190. [PMID: 37469989 PMCID: PMC10352831 DOI: 10.3389/fendo.2023.1184190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 06/09/2023] [Indexed: 07/21/2023] Open
Abstract
Objective Diabetic kidney disease (DKD) has been reported as a main microvascular complication of diabetes mellitus. Although renal biopsy is capable of distinguishing DKD from Non Diabetic kidney disease(NDKD), no gold standard has been validated to assess the development of DKD.This study aimed to build an auxiliary diagnosis model for type 2 Diabetic kidney disease (T2DKD) based on machine learning algorithms. Methods Clinical data on 3624 individuals with type 2 diabetes (T2DM) was gathered from January 1, 2019 to December 31, 2019 using a multi-center retrospective database. The data fell into a training set and a validation set at random at a ratio of 8:2. To identify critical clinical variables, the absolute shrinkage and selection operator with the lowest number was employed. Fifteen machine learning models were built to support the diagnosis of T2DKD, and the optimal model was selected in accordance with the area under the receiver operating characteristic curve (AUC) and accuracy. The model was improved with the use of Bayesian Optimization methods. The Shapley Additive explanations (SHAP) approach was used to illustrate prediction findings. Results DKD was diagnosed in 1856 (51.2 percent) of the 3624 individuals within the final cohort. As revealed by the SHAP findings, the Categorical Boosting (CatBoost) model achieved the optimal performance 1in the prediction of the risk of T2DKD, with an AUC of 0.86 based on the top 38 characteristics. The SHAP findings suggested that a simplified CatBoost model with an AUC of 0.84 was built in accordance with the top 12 characteristics. The more basic model features consisted of systolic blood pressure (SBP), creatinine (CREA), length of stay (LOS), thrombin time (TT), Age, prothrombin time (PT), platelet large cell ratio (P-LCR), albumin (ALB), glucose (GLU), fibrinogen (FIB-C), red blood cell distribution width-standard deviation (RDW-SD), as well as hemoglobin A1C(HbA1C). Conclusion A machine learning-based model for the prediction of the risk of developing T2DKD was built, and its effectiveness was verified. The CatBoost model can contribute to the diagnosis of T2DKD. Clinicians could gain more insights into the outcomes if the ML model is made interpretable.
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Affiliation(s)
- Xiao zhu Liu
- Department of Cardiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Medical Data Science Academy, Chongqing Medical University, Chongqing, China
| | - Minjie Duan
- Medical Data Science Academy, Chongqing Medical University, Chongqing, China
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Hao dong Huang
- Medical Data Science Academy, Chongqing Medical University, Chongqing, China
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Yang Zhang
- Medical Data Science Academy, Chongqing Medical University, Chongqing, China
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Tian yu Xiang
- Information Center, The University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Wu ceng Niu
- Department of Nuclear Medicine, Handan First Hospital, Hebei, China
| | - Bei Zhou
- Department of Cardiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hao lin Wang
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Ting ting Zhang
- Department of Endocrinology, Fifth Medical Center of Chinese People's Liberation Army (PLA) Hospital, Beijing, China
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12
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Taha MM, Mahdy-Abdallah H, Shahy EM, Helmy MA, ElLaithy LS. Diagnostic efficacy of cystatin-c in association with different ACE genes predicting renal insufficiency in T2DM. Sci Rep 2023; 13:5288. [PMID: 37002266 PMCID: PMC10066320 DOI: 10.1038/s41598-023-32012-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/21/2023] [Indexed: 04/04/2023] Open
Abstract
Diabetic nephropathy (DN) seems to be the major cause of chronic kidney disease that may finally lead to End Stage Renal Disease. So, renal function assessment in type 2 diabetes mellitus (T2DM) individuals is very important. Clearly, DN pathogenesis is multifactorial and different proteins, genes and environmental factors can contribute to the onset of the disease. We assessed sensitive and specific biomarkers (in blood and urine) which can predict kidney disease susceptibility among T2DM patients. Serum cystatin-c (cyst-c) in blood and urinary hemeoxygenase (HO-1) in addition to ACE I/D polymorphism and ACE G2350A genotypes. Hundred and eight T2DM patients and 85 controls were enrolled. Serum cystatin-c and urinary (HO-1) were tested by ELISA. Genetic determination of both ACE I/D polymorphism and ACE G2350A genotypes was performed by PCR for all participants. Significant rise in serum cystatin-c and urinary HO-1 levels were shown in diabetic groups compared with control group. Moreover, GG genotype of ACE G2350A gene in diabetic group was associated with rise in serum cystatin-c and urinary HO-1 compared with control group. Mutant AA genotype demonstrated increase in urinary HO-1. DD polymorphism was associated with rise in serum creatinine and cyst-c in diabetic group. Positive correlation was seen between duration of diabetes and serum cyst-c and between serum glucose and urinary (HO-1) in diabetic group. The results from this study indicated an association of serum cystatin-c with GG genotype of ACE G2350A in conjugation with DD polymorphism of ACE I/D which could be an early predictor of tubular injury in T2DM diabetic patients.
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Affiliation(s)
- Mona Mohamed Taha
- Department of Environmental and Occupational Medicine, National Research Centre, Dokki, Cairo, Egypt.
| | - Heba Mahdy-Abdallah
- Department of Environmental and Occupational Medicine, National Research Centre, Dokki, Cairo, Egypt
| | - Eman Mohamed Shahy
- Department of Environmental and Occupational Medicine, National Research Centre, Dokki, Cairo, Egypt
| | - Mona Adel Helmy
- Department of Environmental and Occupational Medicine, National Research Centre, Dokki, Cairo, Egypt
| | - Lamia Samir ElLaithy
- Department of Environmental and Occupational Medicine, National Research Centre, Dokki, Cairo, Egypt
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13
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Chen F, Kantagowit P, Nopsopon T, Chuklin A, Pongpirul K. Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance. PLoS One 2023; 18:e0278729. [PMID: 36821539 PMCID: PMC9949618 DOI: 10.1371/journal.pone.0278729] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 02/06/2023] [Indexed: 02/24/2023] Open
Abstract
Chronic Kidney disease (CKD) is an important yet under-recognized contributor to morbidity and mortality globally. Machine-learning (ML) based decision support tools have been developed across many aspects of CKD care. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression may help to facilitate early disease prevention, assist with early planning of renal replacement therapy, and offer potential clinical and economic benefits to patients and health systems. Clinical implementation can be affected by the uncertainty surrounding the methodological rigor and performance of ML-based models. This systematic review aims to evaluate the application of prognostic and diagnostic ML tools in CKD development and progression. The protocol has been prepared using the Preferred Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. The systematic review protocol for CKD prediction and diagnosis have been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022356704, CRD42022372378). A systematic search will be undertaken of PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the Web of Science, and the IEEE Xplore digital library. Studies in which ML has been applied to predict and diagnose CKD development and progression will be included. The primary outcome will be the comparison of the performance of ML-based models with non-ML-based models. Secondary analysis will consist of model use cases, model construct, and model reporting quality. This systematic review will offer valuable insight into the performance and reporting quality of ML-based models in CKD diagnosis and prediction. This will inform clinicians and technical specialists of the current development of ML in CKD care, as well as direct future model development and standardization.
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Affiliation(s)
- Fangyue Chen
- Global Health Partnerships, Health Education England, London, United Kingdom
- School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | | | - Tanawin Nopsopon
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Arisa Chuklin
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Krit Pongpirul
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Clinical Research Center, Bumrungrad International Hospital, Bangkok, Thailand
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14
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Hosseini Sarkhosh SM, Hemmatabadi M, Esteghamati A. Development and validation of a risk score for diabetic kidney disease prediction in type 2 diabetes patients: a machine learning approach. J Endocrinol Invest 2023; 46:415-423. [PMID: 36114952 DOI: 10.1007/s40618-022-01919-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 09/08/2022] [Indexed: 01/25/2023]
Abstract
PURPOSE This study aims to develop and validate a risk score to predict the occurrence of DKD in individuals with type 2 diabetes using a machine learning (ML) approach. METHODS By implementing Recursive Feature Elimination with Cross-Validation (RFECV) and RFE on the Diabetes Clinic of Imam Khomeini Hospital Complex (IKHC) dataset, the most critical features were identified. These features were used in the multivariate logistic regression (LR) analysis, and the discrimination and calibration of the model were evaluated. Finally, external validation of the model was assessed. RESULTS The development dataset included 1907 type 2 diabetic patients, 763 of whom developed DKD over 5 years. The predictive model performed well in the development dataset by implementing RFECV with the RF algorithm and considering six features (AUC: 79%). Using these features, the LR-based risk score indicated appropriate discrimination (AUC: 75.5%, 95% CI 73-78%) and acceptable calibration ([Formula: see text]= 7.44; p value = 0.49). This risk score was then used for 1543 diabetic patients in the validation dataset, including 633 patients with DKD over 5 years. The results showed sufficient discrimination (AUC: 75.8%, 95% CI 73-78%) of the risk score in the validation dataset. CONCLUSIONS We developed and validated a new risk score for predicting DKD via ML approach, which used common features in the periodic screening of type 2 diabetic patients that are readily available. In addition, a web-based online tool that is readily available to the public was developed to calculate the DKD risk score.
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Affiliation(s)
| | - M Hemmatabadi
- Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - A Esteghamati
- Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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15
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Huang J, Yeung AM, Armstrong DG, Battarbee AN, Cuadros J, Espinoza JC, Kleinberg S, Mathioudakis N, Swerdlow MA, Klonoff DC. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. J Diabetes Sci Technol 2023; 17:224-238. [PMID: 36121302 PMCID: PMC9846408 DOI: 10.1177/19322968221124583] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.
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Affiliation(s)
| | | | - David G. Armstrong
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - Ashley N. Battarbee
- Center for Women’s Reproductive Health,
The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jorge Cuadros
- Meredith Morgan Optometric Eye Center,
University of California, Berkeley, Berkeley, CA, USA
| | - Juan C. Espinoza
- Children’s Hospital Los Angeles,
University of Southern California, Los Angeles, CA, USA
| | | | | | - Mark A. Swerdlow
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - David C. Klonoff
- Diabetes Technology Society,
Burlingame, CA, USA
- Diabetes Research Institute,
Mills-Peninsula Medical Center, San Mateo, CA, USA
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16
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Sun L, Wu Y, Hua RX, Zou LX. Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus. Ren Fail 2022; 44:1454-1461. [PMID: 36036430 PMCID: PMC9427038 DOI: 10.1080/0886022x.2022.2113797] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
Abstract
BACKGROUND Diabetic kidney disease (DKD) is a common and serious complication in patients with diabetic mellitus (DM), the risk of cardiovascular events and all-cause mortality also increases in DKD patients. This study aimed to detect the influencing factors of DKD in type 2 DM (T2DM) patients, and construct DKD prediction models and nomogram for clinical decision-making. METHODS A total of 14,628 patients with T2DM were included. These patients were divided into pre-DKD and non-DKD groups, depending on the occurrence of DKD during a 3-year follow-up from first clinic attendance. The influencing indicators of DKD were analyzed, the prediction models were established by multivariable logistic regression, and a nomogram was drawn for DKD risk assessment. RESULTS Two prediction models for DKD were built by multivariate logistic regression analysis. Model 1 was created based on 17 variables using the forward selection method, Model 2 was established by 19 variables using the backward elimination method. The Somers' D values of both models were 0.789. Four independent predictors were selected to build the nomogram, including age, UACR, eGFR, and neutrophil percentages. The C-index of the nomogram reached 0.864, suggesting a good predictive accuracy for DKD development. CONCLUSIONS Our prediction models had strong predictive powers, and our nomogram provided visual aids to DKD risk calculation, which was simple and fast. These algorithms can provide early DKD risk prediction, which might help to improve the medical care for early detection and intervention in T2DM patients, and then consequently improve the prognosis of DM patients.
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Affiliation(s)
- Ling Sun
- Department of Nephrology, Xuzhou Central Hospital, Xuzhou, China.,Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
| | - Yu Wu
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
| | - Rui-Xue Hua
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
| | - Lu-Xi Zou
- School of Management, Xuzhou Medical University, Xuzhou, China
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17
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Chao X, Wang S, Lang J, Leng J, Fan Q. The application of risk models based on machine learning to predict endometriosis-associated ovarian cancer in patients with endometriosis. Acta Obstet Gynecol Scand 2022; 101:1440-1449. [PMID: 36210724 PMCID: PMC9812095 DOI: 10.1111/aogs.14462] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/03/2022] [Accepted: 09/09/2022] [Indexed: 01/07/2023]
Abstract
INTRODUCTION There is currently no satisfactory model for predicting malignant transformation of endometriosis. The aim of this study was to construct and evaluate a risk model incorporating noninvasive clinical parameters to predict endometriosis-associated ovarian cancer (EAOC) in patients with endometriosis. MATERIAL AND METHODS We enrolled 6809 patients with endometriosis confirmed by pathology, and randomly allocated them to training (n = 4766) and testing cohorts (n = 2043). The proportion of patients with EAOC in each cohort was similar. We extracted a total of 94 demographic and clinicopathologic features from the medical records using natural language processing. We used a machine learning method - gradient-boosting decision tree - to construct a predictive model for EAOC and to evaluate the accuracy of the model. We also constructed a multivariate logistic regression model inclusive of the EAOC-associated risk factors using a back stepwise procedure. Then we compared the performance of the two risk-predicting models using DeLong's test. RESULTS The occurrence of EAOC was 1.84% in this study. The logistic regression model comprised 10 selected features and demonstrated good discrimination in the testing cohort, with an area under the curve (AUC) of 0.891 (95% confidence interval [CI] 0.821-0.960), sensitivity of 88.9%, and specificity of 76.7%. The risk model based on machine learning had an AUC of 0.942 (95% CI 0.914-0.969), sensitivity of 86.8%, and specificity of 86.7%. The machine learning-based risk model performed better than the logistic regression model in DeLong's test (p = 0.036). Furthermore, in a prospective dataset, the machine learning-based risk model had an AUC of 0.8758, a sensitivity of 94.4%, and a specificity of 73.8%. CONCLUSIONS The machine learning-based risk model was constructed to predict EAOC and had high sensitivity and specificity. This model could be of considerable use in helping reduce medical costs and designing follow-up schedules.
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Affiliation(s)
- Xiaopei Chao
- Department of Obstetrics and GynecologyPeking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical CollegeBeijingChina,National Clinical Research Center for Obstetric & Gynecologic DiseasesBeijingChina
| | - Shu Wang
- Department of Obstetrics and GynecologyPeking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical CollegeBeijingChina,National Clinical Research Center for Obstetric & Gynecologic DiseasesBeijingChina
| | - Jinghe Lang
- Department of Obstetrics and GynecologyPeking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical CollegeBeijingChina,National Clinical Research Center for Obstetric & Gynecologic DiseasesBeijingChina
| | - Jinhua Leng
- Department of Obstetrics and GynecologyPeking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical CollegeBeijingChina,National Clinical Research Center for Obstetric & Gynecologic DiseasesBeijingChina
| | - Qingbo Fan
- Department of Obstetrics and GynecologyPeking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical CollegeBeijingChina,National Clinical Research Center for Obstetric & Gynecologic DiseasesBeijingChina
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18
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Russo GT, Giandalia A, Ceriello A, Di Bartolo P, Di Cianni G, Fioretto P, Giorda CB, Manicardi V, Pontremoli R, Viazzi F, Lucisano G, Nicolucci A, De Cosmo S. A prediction model to assess the risk of egfr loss in patients with type 2 diabetes and preserved kidney function: The amd annals initiative. Diabetes Res Clin Pract 2022; 192:110092. [PMID: 36167264 DOI: 10.1016/j.diabres.2022.110092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/05/2022] [Accepted: 09/19/2022] [Indexed: 11/22/2022]
Abstract
OBJECTIVE To develop and validate a model for predicting 5-year eGFR-loss in type 2 diabetes mellitus (T2DM) patients with preserved renal function at baseline. RESEARCH DESIGN AND METHODS A cohort of 504.532 T2DM outpatients participating to the Medical Associations of Diabetologists (AMD) Annals Initiative was splitted into the Learning and Validation cohorts, in which the predictive model was respectively developed and validated. A multivariate Cox proportional hazard regression model including all baseline characteristics was performed to identify predictors of eGFR-loss. A weight derived from regression coefficients was assigned to each variable and the overall sum of weights determined the 0 to 8-risk score. RESULTS A set of demographic, clinical and laboratory parameters entered the final model. The eGFR-loss score showed a good performance in the Validation cohort. Increasing score values progressively identified a higher risk of GFR loss: a score ≥ 8 was associated with a HR of 13.48 (12.96-14.01) in the Learning and a HR of 13.45 (12.93-13.99) in the Validation cohort. The 5 years-probability of developing the study outcome was 55.9% higher in subjects with a score ≥ 8. CONCLUSIONS In the large AMD Annals Initiative cohort, we developed and validated an eGFR-loss prediction model to identify T2DM patients at risk of developing clinically meaningful renal complications within a 5-years time frame.
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Affiliation(s)
- G T Russo
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy.
| | - A Giandalia
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy.
| | - A Ceriello
- Department of Cardiovascular and Metabolic Diseases, IRCCS Gruppo Multimedica, MI, Italy.
| | | | - G Di Cianni
- Diabetes and Metabolic Diseases Unit, Health Local Unit North-West Tuscany, Livorno, Italy.
| | - P Fioretto
- Department of Medicine, University of Padua, Unit of Medical Clinic 3, Hospital of Padua, Padua, Italy.
| | - C B Giorda
- Diabetes and Metabolism Unit ASL Turin 5 Chieri (TO), Italy.
| | - V Manicardi
- Diabetes Consultant, Salus Hospital, Reggio Emilia, Italy.
| | - R Pontremoli
- Università degli Studi and IRCCS Ospedale Policlinico San Martino, Genova, Italy.
| | - F Viazzi
- Università degli Studi and IRCCS Ospedale Policlinico San Martino, Genova, Italy.
| | - G Lucisano
- Center for Outcomes Research and Clinical Epidemiology, CORESEARCH, Pescara, Italy.
| | - A Nicolucci
- Center for Outcomes Research and Clinical Epidemiology, CORESEARCH, Pescara, Italy.
| | - S De Cosmo
- Department of Medical Sciences, Scientific Institute "Casa Sollievo della Sofferenza", San Giovanni Rotondo (FG), Italy.
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Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation. ADVANCES IN HUMAN-COMPUTER INTERACTION 2022. [DOI: 10.1155/2022/9220560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease.
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20
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Tuppad A, Patil SD. Machine learning for diabetes clinical decision support: a review. ADVANCES IN COMPUTATIONAL INTELLIGENCE 2022; 2:22. [PMID: 35434723 PMCID: PMC9006199 DOI: 10.1007/s43674-022-00034-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 02/27/2022] [Accepted: 03/03/2022] [Indexed: 12/14/2022]
Abstract
Type 2 diabetes has recently acquired the status of an epidemic silent killer, though it is non-communicable. There are two main reasons behind this perception of the disease. First, a gradual but exponential growth in the disease prevalence has been witnessed irrespective of age groups, geography or gender. Second, the disease dynamics are very complex in terms of multifactorial risks involved, initial asymptomatic period, different short-term and long-term complications posing serious health threat and related co-morbidities. Majority of its risk factors are lifestyle habits like physical inactivity, lack of exercise, high body mass index (BMI), poor diet, smoking except some inevitable ones like family history of diabetes, ethnic predisposition, ageing etc. Nowadays, machine learning (ML) is increasingly being applied for alleviation of diabetes health burden and many research works have been proposed in the literature to offer clinical decision support in different application areas as well. In this paper, we present a review of such efforts for the prevention and management of type 2 diabetes. Firstly, we present the medical gaps in diabetes knowledge base, guidelines and medical practice identified from relevant articles and highlight those that can be addressed by ML. Further, we review the ML research works in three different application areas namely—(1) risk assessment (statistical risk scores and ML-based risk models), (2) diagnosis (using non-invasive and invasive features), (3) prognosis (from normoglycemia/prior morbidity to incident diabetes and prognosis of incident diabetes to related complications). We discuss and summarize the shortcomings or gaps in the existing ML methodologies for diabetes to be addressed in future. This review provides the breadth of ML predictive modeling applications for diabetes while highlighting the medical and technological gaps as well as various aspects involved in ML-based diabetes clinical decision support.
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Affiliation(s)
- Ashwini Tuppad
- School of Computer Science and Engineering, REVA University, Rukmini Knowledge Park, Kattigenahalli, Bangalore, Karnataka India
| | - Shantala Devi Patil
- School of Computer Science and Engineering, REVA University, Rukmini Knowledge Park, Kattigenahalli, Bangalore, Karnataka India
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21
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Comparison of Different Machine Learning Techniques to Predict Diabetic Kidney Disease. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7378307. [PMID: 35399848 PMCID: PMC8993553 DOI: 10.1155/2022/7378307] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/10/2022] [Accepted: 03/21/2022] [Indexed: 12/17/2022]
Abstract
Background Diabetic kidney disease (DKD), one of the complications of diabetes in patients, leads to progressive loss of kidney function. Timely intervention is known to improve outcomes. Therefore, screening patients to identify high-risk populations is important. Machine learning classification techniques can be applied to patient datasets to identify high-risk patients by building a predictive model. Objective This study aims to identify a suitable classification technique for predicting DKD by applying different classification techniques to a DKD dataset and comparing their performance using WEKA machine learning software. Methods The performance of nine different classification techniques was analyzed on a DKD dataset with 410 instances and 18 attributes. Data preprocessing was carried out using the PartitionMembershipFilter. A 10-fold cross validation was performed on the dataset. The performance was assessed on the basis of the execution time, accuracy, correctly and incorrectly classified instances, kappa statistics (K), mean absolute error, root mean squared error, and true values of the confusion matrix. Results With an accuracy of 93.6585% and a higher K value (0.8731), IBK and random tree classification techniques were found to be the best performing techniques. Moreover, they also exhibited the lowest root mean squared error rate (0.2496). There were 15 false-positive instances and 11 false-negative instances with these prediction models. Conclusions This study identified IBK and random tree classification techniques as the best performing classifiers and accurate prediction methods for DKD.
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Liu C, Wu K, Gao H, Li J, Xu X. Current Strategies and Potential Prospects for Nanoparticle-Mediated Treatment of Diabetic Nephropathy. Diabetes Metab Syndr Obes 2022; 15:2653-2673. [PMID: 36068795 PMCID: PMC9441178 DOI: 10.2147/dmso.s380550] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/20/2022] [Indexed: 11/23/2022] Open
Abstract
Diabetic nephropathy (DN), a severe microvascular complication of diabetes mellitus (DM), is the most common form of chronic kidney disease (CKD) and a leading cause of renal failure in end-stage renal disease. No currently available treatment can achieve complete cure. Traditional treatments have many limitations, such as painful subcutaneous insulin injections, nephrotoxicity and hepatotoxicity with oral medication, and poor patient compliance with continual medication intake. Given the known drawbacks, recent research has suggested that nanoparticle-based drug delivery platforms as therapeutics may provide a promising strategy for treating debilitating diseases such as DN in the future. This administration method provides multiple advantages, such as delivering the loaded drug to the precise target of action and enabling early prevention of CKD progression. This article discusses the development of the main currently used nanoplatforms, such as liposomes, polymeric NPs, and inorganic NPs, as well as the prospects and drawbacks of nanoplatform application in the treatment of CKD.
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Affiliation(s)
- Chunkang Liu
- Department of Gastrointestinal Surgery, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Kunzhe Wu
- Department of Scientific Research Center, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Huan Gao
- Department of Nephrology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Jianyang Li
- Department of Nephrology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Xiaohua Xu
- Department of Nephrology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
- Correspondence: Xiaohua Xu, Email
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