1
|
Díez-Sanmartín C, Sarasa Cabezuelo A, Andrés Belmonte A. Ensemble of machine learning techniques to predict survival in kidney transplant recipients. Comput Biol Med 2024; 180:108982. [PMID: 39111152 DOI: 10.1016/j.compbiomed.2024.108982] [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: 08/21/2023] [Revised: 04/01/2024] [Accepted: 08/01/2024] [Indexed: 08/29/2024]
Abstract
Kidney transplant recipients face a high cardiovascular risk, which is a leading cause of death in this patient group. This article proposes the application of clustering techniques and feature selection to predict the survival outcomes of kidney transplant recipients based on machine learning techniques and mainstream statistical methods. First, feature selection techniques (Boruta, Random Survival Forest and Elastic Net) are used to detect the most relevant variables. Subsequently, each set of variables obtained by each feature selection technique is used as input for the clustering algorithms used (Consensus Clustering, Self-Organizing Map and Agglomerative Clustering) to determine which combination of feature selection, clustering algorithm and number of clusters maximizes intercluster variability. Next, the mechanism called False Clustering Discovery Reduction is applied to obtain the minimum number of statistically differentiable populations after applying a control metric. This metric is based on a variance test to confirm that reducing the number of clusters does not generate significant losses in the heterogeneity obtained. This approach was applied to the Organ Procurement and Transplantation Network medical dataset (n = 11,332). The combination of Random Survival Forest and consensus clustering yielded the optimal result of 4 clusters starting from 8 initial ones. Finally, for each population, Kaplan-Meier survival curves are generated to predict the survival of new patients based on the predictions of the XGBoost classifier, with an overall multi-class AUC of 98.11%.
Collapse
Affiliation(s)
- Covadonga Díez-Sanmartín
- Department of Computer Systems and Computing, School of Computer Science, Complutense University of Madrid, 28040, Madrid, Spain.
| | - Antonio Sarasa Cabezuelo
- Department of Computer Systems and Computing, School of Computer Science, Complutense University of Madrid, 28040, Madrid, Spain
| | - Amado Andrés Belmonte
- Nephrology Department, 12 de Octubre Hospital, Complutense University of Madrid, 28041, Madrid, Spain.
| |
Collapse
|
2
|
Acharya PC, Alba R, Krisanapan P, Acharya CM, Suppadungsuk S, Csongradi E, Mao MA, Craici IM, Miao J, Thongprayoon C, Cheungpasitporn W. AI-Driven Patient Education in Chronic Kidney Disease: Evaluating Chatbot Responses against Clinical Guidelines. Diseases 2024; 12:185. [PMID: 39195184 DOI: 10.3390/diseases12080185] [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: 06/18/2024] [Revised: 08/10/2024] [Accepted: 08/13/2024] [Indexed: 08/29/2024] Open
Abstract
Chronic kidney disease (CKD) patients can benefit from personalized education on lifestyle and nutrition management strategies to enhance healthcare outcomes. The potential use of chatbots, introduced in 2022, as a tool for educating CKD patients has been explored. A set of 15 questions on lifestyle modification and nutrition, derived from a thorough review of three specific KDIGO guidelines, were developed and posed in various formats, including original, paraphrased with different adverbs, incomplete sentences, and misspellings. Four versions of AI were used to answer these questions: ChatGPT 3.5 (March and September 2023 versions), ChatGPT 4, and Bard AI. Additionally, 20 questions on lifestyle modification and nutrition were derived from the NKF KDOQI guidelines for nutrition in CKD (2020 Update) and answered by four versions of chatbots. Nephrologists reviewed all answers for accuracy. ChatGPT 3.5 produced largely accurate responses across the different question complexities, with occasional misleading statements from the March version. The September 2023 version frequently cited its last update as September 2021 and did not provide specific references, while the November 2023 version did not provide any misleading information. ChatGPT 4 presented answers similar to 3.5 but with improved reference citations, though not always directly relevant. Bard AI, while largely accurate with pictorial representation at times, occasionally produced misleading statements and had inconsistent reference quality, although an improvement was noted over time. Bing AI from November 2023 had short answers without detailed elaboration and sometimes just answered "YES". Chatbots demonstrate potential as personalized educational tools for CKD that utilize layman's terms, deliver timely and rapid responses in multiple languages, and offer a conversational pattern advantageous for patient engagement. Despite improvements observed from March to November 2023, some answers remained potentially misleading. ChatGPT 4 offers some advantages over 3.5, although the differences are limited. Collaboration between healthcare professionals and AI developers is essential to improve healthcare delivery and ensure the safe incorporation of chatbots into patient care.
Collapse
Affiliation(s)
- Prakrati C Acharya
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA
| | - Raul Alba
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA
| | - Pajaree Krisanapan
- Division of Nephrology, Thammasat University Hospital, Pathum Thani 12120, Thailand
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA
| | - Chirag M Acharya
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Eva Csongradi
- Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Iasmina M Craici
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA
| | | |
Collapse
|
3
|
Zhuang K, Wang W, Xu C, Guo X, Ren X, Liang Y, Duan Z, Song Y, Zhang Y, Cai G. Machine learning-based diagnosis and prognosis of IgAN: A systematic review and meta-analysis. Heliyon 2024; 10:e33090. [PMID: 38988582 PMCID: PMC11234108 DOI: 10.1016/j.heliyon.2024.e33090] [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: 08/09/2023] [Revised: 06/04/2024] [Accepted: 06/13/2024] [Indexed: 07/12/2024] Open
Abstract
Purpose Plenty of studies have explored the diagnosis and prognosis of IgA nephropathy (IgAN) based on machine learning (ML), but the accuracy lacks the support of evidence-based medical evidence. We aim at this problem to guide the precision treatment of IgAN. Methods Embase, Pubmed, Cochrane Library, and Web of Science were searched systematically until February 24th, 2024, for publications on ML-based diagnosis and prognosis of IgAN. Subgroup analysis or meta-regression was conducted according to modeling method, follow-up time, endpoint definition, and variable type. Further, the rank sum test was applied to compare the discrimination ability of prognosis. Results A total of 47 studies involving 51,935 patients were eligible. Among the 38 diagnostic models, the pooled C-index was 0.902 (95 % CI: 0.878-0.926) in 27 diagnostic models. Of the 162 prognostic models, the C-index for model discrimination of 144 prognostic models was 0.838 (95 % CI: 0.827-0.850) in training. The overall discrimination ability of prognosis was as follows: COX regression > new ML models (e.g. ANN, DT, RF, SVM, XGBoost) > traditional ML models (logistic regression) > Naïve Bayesian network (P < 0.05). External validation of IIgAN-RPT in 19 models showed a pooled C-index of 0.801 (95 % CI: 0.784-0.817). Conclusions New ML models have shown application values that are as good as traditional ML models, both in diagnosis and prognosis. In addition, future models are desired to use a more sensitive prognostic endpoint (albuminuria), improve predictive ability in moderate progression risk, and ultimately translate into clinically applicable intelligent tools.
Collapse
Affiliation(s)
- Kaiting Zhuang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Wenjuan Wang
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Cheng Xu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Xinru Guo
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Xuejing Ren
- Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Henan Key Laboratory of Kidney Disease and Immunology, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Yanjun Liang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Zhiyu Duan
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Yanqi Song
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Yifan Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Guangyan Cai
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| |
Collapse
|
4
|
Khalid F, Alsadoun L, Khilji F, Mushtaq M, Eze-Odurukwe A, Mushtaq MM, Ali H, Farman RO, Ali SM, Fatima R, Bokhari SFH. Predicting the Progression of Chronic Kidney Disease: A Systematic Review of Artificial Intelligence and Machine Learning Approaches. Cureus 2024; 16:e60145. [PMID: 38864072 PMCID: PMC11166249 DOI: 10.7759/cureus.60145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2024] [Indexed: 06/13/2024] Open
Abstract
Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This systematic review comprehensively evaluates the application of artificial intelligence (AI) and machine learning (ML) techniques for predicting CKD progression. A rigorous literature search identified 13 relevant studies employing diverse AI/ML algorithms, including logistic regression, support vector machines, random forests, neural networks, and deep learning approaches. These studies primarily aimed to predict CKD progression to end-stage renal disease (ESRD) or the need for renal replacement therapy, with some focusing on diabetic kidney disease progression, proteinuria, or estimated glomerular filtration rate (GFR) decline. The findings highlight the promising predictive performance of AI/ML models, with several achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve scores. Key factors contributing to enhanced prediction included incorporating longitudinal data, baseline characteristics, and specific biomarkers such as estimated GFR, proteinuria, serum albumin, and hemoglobin levels. Integration of these predictive models with electronic health records and clinical decision support systems offers opportunities for timely risk identification, early interventions, and personalized management strategies. While challenges related to data quality, bias, and ethical considerations exist, the reviewed studies underscore the potential of AI/ML techniques to facilitate early detection, risk stratification, and targeted interventions for CKD patients. Ongoing research, external validation, and careful implementation are crucial to leveraging these advanced analytical approaches in clinical practice, ultimately improving outcomes and reducing the burden of CKD.
Collapse
Affiliation(s)
- Fizza Khalid
- Nephrology, Sharif Medical City Hospital, Lahore, PAK
| | - Lara Alsadoun
- Trauma and Orthopedics, Chelsea and Westminster Hospital, London, GBR
| | - Faria Khilji
- Internal Medicine, Tehsil Headquarter Hospital, Shakargarh, PAK
- Internal Medicine, Quaid-e-Azam Medical College, Bahawalpur, PAK
| | - Maham Mushtaq
- Medicine and Surgery, King Edward Medical University, Lahore, PAK
| | | | | | - Husnain Ali
- Medicine and Surgery, King Edward Medical University, Lahore, PAK
| | - Rana Omer Farman
- Medicine and Surgery, King Edward Medical University, Lahore, PAK
| | - Syed Momin Ali
- Medicine and Surgery, King Edward Medical University, Lahore, PAK
| | - Rida Fatima
- Medicine and Surgery, Fatima Jinnah Medical University, Lahore, PAK
| | | |
Collapse
|
5
|
Hirsch JS, Danna SC, Desai N, Gluckman TJ, Jhamb M, Newlin K, Pellechio B, Elbedewe A, Norfolk E. Optimizing Care Delivery in Patients with Chronic Kidney Disease in the United States: Proceedings of a Multidisciplinary Roundtable Discussion and Literature Review. J Clin Med 2024; 13:1206. [PMID: 38592013 PMCID: PMC10932233 DOI: 10.3390/jcm13051206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/07/2024] [Accepted: 02/10/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Approximately 37 million individuals in the United States (US) have chronic kidney disease (CKD). Patients with CKD have a substantial morbidity and mortality, which contributes to a huge economic burden to the healthcare system. A limited number of clinical pathways or defined workflows exist for CKD care delivery in the US, primarily due to a lower prioritization of CKD care within health systems compared with other areas (e.g., cardiovascular disease [CVD], cancer screening). CKD is a public health crisis and by the year 2040, CKD will become the fifth leading cause of years of life lost. It is therefore critical to address these challenges to improve outcomes in patients with CKD. METHODS The CKD Leaders Network conducted a virtual, 3 h, multidisciplinary roundtable discussion with eight subject-matter experts to better understand key factors impacting CKD care delivery and barriers across the US. A premeeting survey identified topics for discussion covering the screening, diagnosis, risk stratification, and management of CKD across the care continuum. Findings from this roundtable are summarized and presented herein. RESULTS Universal challenges exist across health systems, including a lack of awareness amongst providers and patients, constrained care team bandwidth, inadequate financial incentives for early CKD identification, non-standardized diagnostic classification and triage processes, and non-centralized patient information. Proposed solutions include highlighting immediate and long-term financial implications linked with failure to identify and address at-risk individuals, identifying and managing early-stage CKD, enhancing efforts to support guideline-based education for providers and patients, and capitalizing on next-generation solutions. CONCLUSIONS Payers and other industry stakeholders have opportunities to contribute to optimal CKD care delivery. Beyond addressing the inadequacies that currently exist, actionable tactics can be implemented into clinical practice to improve clinical outcomes in patients at risk for or diagnosed with CKD in the US.
Collapse
Affiliation(s)
- Jamie S. Hirsch
- Northwell Health, Northwell Health Physician Partners, 100 Community Drive, Floor 2, Great Neck, NY 11021, USA
| | - Samuel Colby Danna
- VA Southeast Louisiana Healthcare System, 2400 Canal Street, New Orleans, LA 70119, USA
| | - Nihar Desai
- Section of Cardiovascular Medicine, Yale School of Medicine, 800 Howard Avenue, Ste 2nd Floor, New Haven, CT 06519, USA
| | - Ty J. Gluckman
- Providence Heart Institute, Center for Cardiovascular Analytics, Research, and Data Science (CARDS), 9205 SW Barnes Road, Suite 598, Portland, OR 97225, USA
| | - Manisha Jhamb
- Division of Renal-Electrolyte, University of Pittsburgh, 3550 Terrace St., Scaife A915, Pittsburgh, PA 15261, USA
| | - Kim Newlin
- Sutter Health, Sutter Roseville Medical Center, 1 Medical Plaza Drive, Roseville, CA 95661, USA
| | - Bob Pellechio
- RWJ Barnabas Health, Cooperman Barnabas Medical Center, 95 Old Short Hills Rd., West Orange, NJ 07052, USA
| | - Ahlam Elbedewe
- The Kinetix Group, 29 Broadway 26th Floor, New York, NY 10006, USA
| | - Evan Norfolk
- Geisinger Medical Center—Nephrology, 100 North Academy Avenue, Danville, PA 17822, USA
| |
Collapse
|
6
|
Wu CC, Islam MM, Poly TN, Weng YC. Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research. Diagnostics (Basel) 2024; 14:397. [PMID: 38396436 PMCID: PMC10887584 DOI: 10.3390/diagnostics14040397] [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: 12/04/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.
Collapse
Affiliation(s)
- Chieh-Chen Wu
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan;
| | - Md. Mohaimenul Islam
- Outcomes and Translational Sciences, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA;
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan;
| | - Yung-Ching Weng
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan;
| |
Collapse
|
7
|
Zheng JX, Li X, Zhu J, Guan SY, Zhang SX, Wang WM. Interpretable machine learning for predicting chronic kidney disease progression risk. Digit Health 2024; 10:20552076231224225. [PMID: 38235416 PMCID: PMC10793198 DOI: 10.1177/20552076231224225] [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] [Accepted: 12/15/2023] [Indexed: 01/19/2024] Open
Abstract
Objective Chronic kidney disease (CKD) poses a major global health burden. Early CKD risk prediction enables timely interventions, but conventional models have limited accuracy. Machine learning (ML) enhances prediction, but interpretability is needed to support clinical usage with both in diagnostic and decision-making. Methods A cohort of 491 patients with clinical data was collected for this study. The dataset was randomly split into an 80% training set and a 20% testing set. To achieve the first objective, we developed four ML algorithms (logistic regression, random forests, neural networks, and eXtreme Gradient Boosting (XGBoost)) to classify patients into two classes-those who progressed to CKD stages 3-5 during follow-up (positive class) and those who did not (negative class). For the classification task, the area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance in discriminating between the two classes. For survival analysis, Cox proportional hazards regression (COX) and random survival forests (RSFs) were employed to predict CKD progression, and the concordance index (C-index) and integrated Brier score were used for model evaluation. Furthermore, variable importance, partial dependence plots, and restrict cubic splines were used to interpret the models' results. Results XGBOOST demonstrated the best predictive performance for CKD progression in the classification task, with an AUC-ROC of 0.867 (95% confidence interval (CI): 0.728-0.100), outperforming the other ML algorithms. In survival analysis, RSF showed slightly better discrimination and calibration on the test set compared to COX, indicating better generalization to new data. Variable importance analysis identified estimated glomerular filtration rate, age, and creatinine as the most important predictors for CKD survival analysis. Further analysis revealed non-linear associations between age and CKD progression, suggesting higher risks in patients aged 52-55 and 65-66 years. The association between cholesterol levels and CKD progression was also non-linear, with lower risks observed when cholesterol levels were in the range of 5.8-6.4 mmol/L. Conclusions Our study demonstrated the effectiveness of interpretable ML models for predicting CKD progression. The comparison between COX and RSF highlighted the advantages of ML in survival analysis, particularly in handling non-linearity and high-dimensional data. By leveraging interpretable ML for unraveling risk factor relationships, contrasting predictive techniques, and exposing non-linear associations, this study significantly advances CKD risk prediction to enable enhanced clinical decision-making.
Collapse
Affiliation(s)
- Jin-Xin Zheng
- Department of Nephrology, Ruijin Hospital, Institute of Nephrology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin Li
- Department of Nephrology, Ruijin Hospital, Institute of Nephrology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiang Zhu
- Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu, China
| | - Shi-Yang Guan
- Department of Statistics, Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Shun-Xian Zhang
- School of Global Health, Chinese Center for Tropical Diseases Research – Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wei-Ming Wang
- Department of Nephrology, Ruijin Hospital, Institute of Nephrology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
8
|
Ruan X, Wang L, Thongprayoon C, Cheungpasitporn W, Liu H. GRU-D-Weibull: A novel real-time individualized endpoint prediction. Artif Intell Med 2023; 146:102696. [PMID: 38042597 DOI: 10.1016/j.artmed.2023.102696] [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: 01/17/2023] [Revised: 10/17/2023] [Accepted: 10/29/2023] [Indexed: 12/04/2023]
Abstract
BACKGROUND In the era of healthcare digital transformation, using electronic health record (EHR) data to generate various endpoint estimates for active monitoring is highly desirable in chronic disease management. However, traditional predictive modeling strategies leveraging well-curated data sets can have limited real-world implementation potential due to various data quality issues in EHR data. METHODS We propose a novel predictive modeling approach, GRU-D-Weibull, which models Weibull distribution leveraging gated recurrent units with decay (GRU-D), for real-time individualized endpoint prediction and population level risk management using EHR data. EXPERIMENTS We systematically evaluated the performance and showcased the real-world implementability of the proposed approach through individual level endpoint prediction using a cohort of patients with chronic kidney disease stage 4 (CKD4). A total of 536 features including ICD/CPT codes, medications, lab tests, vital measurements, and demographics were retrieved for 6879 CKD4 patients. The performance metrics including C-index, L1-loss, Parkes' error, and predicted survival probability at time of event were compared between GRU-D-Weibull and other alternative approaches including accelerated failure time model (AFT), XGBoost based AFT (XGB(AFT)), random survival forest (RSF), and Nnet-survival. Both in-process and post-process calibrations were experimented on GRU-D-Weibull generated survival probabilities. RESULTS GRU-D-Weibull demonstrated C-index of ~0.7 at index date, which increased to ~0.77 at 4.3 years of follow-up, comparable to that of RSF. GRU-D-Weibull achieved absolute L1-loss of ~1.1 years (sd≈0.95) at CKD4 index date, and a minimum of ~0.45 year (sd≈0.3) at 4 years of follow-up, comparing to second-ranked RSF of ~1.4 years (sd≈1.1) at index date and ~0.64 years (sd≈0.26) at 4 years. Both significantly outperform competing approaches. GRU-D-Weibull constrained predicted survival probability at time of event to smaller and more fixed range than competing models throughout follow-up. Significant correlations were observed between prediction error and missing proportions of all major categories of input features at index date (Corr ~0.1 to ~0.3), which faded away within 1 year after index date as more data became available. Through post training recalibration, we achieved a close alignment between the predicted and observed survival probabilities across multiple prediction horizons at different time points during follow-up. CONCLUSION GRU-D-Weibull shows advantages over competing methods in handling missingness commonly encountered in EHR data and providing both probability and point estimates for diverse prediction horizons during follow-up. The experiment highlights the potential of GRU-D-Weibull as a suitable candidate for individualized endpoint risk management, utilizing real-time clinical data to generate various endpoint estimates for monitoring. Additional research is warranted to evaluate the influence of different data quality aspects on prediction performance. Furthermore, collaboration with clinicians is essential to explore the integration of this approach into clinical workflows and evaluate its effects on decision-making processes and patient outcomes.
Collapse
Affiliation(s)
- Xiaoyang Ruan
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States; Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States
| | - Liwei Wang
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States; Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States
| | | | | | - Hongfang Liu
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States; Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
| |
Collapse
|
9
|
Ganie SM, Dutta Pramanik PK, Mallik S, Zhao Z. Chronic kidney disease prediction using boosting techniques based on clinical parameters. PLoS One 2023; 18:e0295234. [PMID: 38039306 PMCID: PMC10691694 DOI: 10.1371/journal.pone.0295234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 11/17/2023] [Indexed: 12/03/2023] Open
Abstract
Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance.
Collapse
Affiliation(s)
- Shahid Mohammad Ganie
- AI Research Centre, School of Business, Woxsen University, Hyderabad, Telangana, India
| | | | - Saurav Mallik
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, United States of America
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
| |
Collapse
|
10
|
Hou X, Tian C, Liu W, Li Y, Li W, Wang Z. Construction of artificial intelligence non-invasive diagnosis model for common glomerular diseases based on hyperspectral and urine analysis. Photodiagnosis Photodyn Ther 2023; 44:103736. [PMID: 37597684 DOI: 10.1016/j.pdpdt.2023.103736] [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: 06/13/2023] [Revised: 07/26/2023] [Accepted: 08/04/2023] [Indexed: 08/21/2023]
Abstract
OBJECTIVE To develop a non-invasive fluid biopsy assisted diagnosis model for glomerular diseases based on hyperspectral, so as to solve the problem of poor compliance of patients with invasive examination and improve the early diagnosis rate of glomerular diseases. METHODS A total of 65 urine samples from patients who underwent renal biopsy from November 2020 to January 2022 in Qianfoshan Hospital of Shandong Province were collected.By simultaneously capturing spectral information of the above urine samples in the 400-1000 nm range, more obvious differences were found in the spectra of urine from patients with glomerular diseases between 650 nm and 680 nm. We obtained the original hyperspectral images in this wavelength range through digital scanning, and sampled pixel points at intervals on the original images. The two-dimensional digital image generated from each pixel point served as a member of the subsequent training and test sets. . After manually labeling the images according to different biopsy pathological types, they were randomly divided into training set (n = 58,800) and test set (n = 25,200). The training set was used for training learning and parameter iteration of artificial intelligence non-invasive liquid diagnosis model, and the test set for model recognition and interpretation. The evaluation indexes such as accuracy, sensitivity and specificity were calculated to evaluate the performance of the diagnosis model. RESULTS The model has an accuracy rate of 96% for early diagnosis of four glomerular diseases. CONCLUSION The auxiliary diagnosis model system has high accuracy. It is expected to be used as a non-invasive diagnostic method for glomerular diseases in clinic.
Collapse
Affiliation(s)
- Xiangyu Hou
- Department of Nephrology, Shandong Institute of Nephrology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong 250014, China
| | - Chongxuan Tian
- Department of biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250016, China
| | - Wen Liu
- Department of Nephrology, Shandong Institute of Nephrology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong 250014, China
| | - Yang Li
- Department of Nephrology, Shandong Institute of Nephrology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong 250014, China
| | - Wei Li
- Department of biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250016, China.
| | - Zunsong Wang
- Department of Nephrology, Shandong Institute of Nephrology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong 250014, China.
| |
Collapse
|
11
|
Qarajeh A, Tangpanithandee S, Thongprayoon C, Suppadungsuk S, Krisanapan P, Aiumtrakul N, Garcia Valencia OA, Miao J, Qureshi F, Cheungpasitporn W. AI-Powered Renal Diet Support: Performance of ChatGPT, Bard AI, and Bing Chat. Clin Pract 2023; 13:1160-1172. [PMID: 37887080 PMCID: PMC10605499 DOI: 10.3390/clinpract13050104] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 09/15/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
Patients with chronic kidney disease (CKD) necessitate specialized renal diets to prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive assessment of food components is pivotal, yet burdensome for healthcare providers. With evolving artificial intelligence (AI) technology, models such as ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and assisting professionals. To gauge the efficacy of different AI models in discerning potassium and phosphorus content in foods, four AI models-ChatGPT 3.5, ChatGPT 4, Bard AI, and Bing Chat-were evaluated. A total of 240 food items, curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input into each model. These items were characterized by their potassium (149 items) and phosphorus (91 items) content. Each model was tasked to categorize the items into high or low potassium and high phosphorus content. The results were juxtaposed with the Mayo Clinic Renal Diet Handbook's recommendations. The concordance between repeated sessions was also evaluated to assess model consistency. Among the models tested, ChatGPT 4 displayed superior performance in identifying potassium content, correctly classifying 81% of the foods. It accurately discerned 60% of low potassium and 99% of high potassium foods. In comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse range of accuracy in discerning potassium and phosphorus content in foods suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement over its predecessor, especially in detecting potassium content. The Bard AI model exhibited exceptional precision for phosphorus identification. This study underscores the potential of AI models as efficient tools in renal dietary planning, though refinements are warranted for optimal utility.
Collapse
Affiliation(s)
- Ahmad Qarajeh
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
- Faculty of Medicine, University of Jordan, Amman 11942, Jordan
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Noppawit Aiumtrakul
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA;
| | - Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (A.Q.); (C.T.); (S.S.); (P.K.); (O.A.G.V.); (J.M.); (F.Q.)
| |
Collapse
|
12
|
Hardman TC, Aitchison R, Scaife R, Edwards J, Slater G. The future of clinical trials and drug development: 2050. Drugs Context 2023; 12:2023-2-2. [PMID: 37313038 PMCID: PMC10259497 DOI: 10.7573/dic.2023-2-2] [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: 02/02/2023] [Accepted: 04/26/2023] [Indexed: 06/15/2023] Open
Abstract
A workshop held at the 18th Annual Conference of the Pharmaceutical Contract Management Group in Krakow on 9 September 2022 asked over 200 delegates what the clinical trial landscape would look like in 2050. Issues considered included who will be running the pharmaceutical industry in 2050; how 'health chips', wearables and diagnostics will impact on finding the right patients to study; how will artificial intelligence be designing and controlling clinical trials; and what will the role of the Clinical Research Associate, the critical observer, documenter and conductor of a clinical trial need to look like by 2050. The consensus was that, by 2050, if you are working in clinical trials, you will be a data scientist. We can expect to see an increasing role of new technologies and a new three-phase registration model for novel therapies. The first phase will involve an aspect of quality evaluation and biological proof-of-concept probably involving more preclinical modelling and engineered human cell lines and fewer animal studies than currently used. Once registered, new products will enter a period of adaptive clinical development (delivered as a single study) intended to establish safety. This phase will most likely take around 1-2 years and explore tailored options for administration. Investigations will most likely be conducted in patients, possibly in a 'patient-in-a-box' setting (hospital or healthcare centre, virtual or microsite). On completion of safety licencing, drugs will begin an assessment of efficacy in partnership with those responsible for reimbursement - testing will be performed in patients, possibly where individual patient involvement in safety testing will offer some reimbursement deal for future treatment. Change is coming, though its precise form will likely depend on the creativity and vision of sponsors, regulators and payers.
Collapse
Affiliation(s)
| | - Rob Aitchison
- 4C Life Sciences, 4C Associates Ltd., Hammersmith, London, UK
| | | | | | - Gill Slater
- Leo Pharma, 2 rue Rene Caudron, Saint Quentin en Yvelines Cedex, France
| |
Collapse
|
13
|
Zhao D, Wang W, Tang T, Zhang YY, Yu C. Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review. Comput Struct Biotechnol J 2023; 21:3315-3326. [PMID: 37333860 PMCID: PMC10275698 DOI: 10.1016/j.csbj.2023.05.029] [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: 10/28/2022] [Revised: 05/28/2023] [Accepted: 05/28/2023] [Indexed: 06/20/2023] Open
Abstract
Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.
Collapse
Affiliation(s)
- Dan Zhao
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Wei Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Tian Tang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Ying-Ying Zhang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Chen Yu
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| |
Collapse
|
14
|
Yoo KD, Noh J, Bae W, An JN, Oh HJ, Rhee H, Seong EY, Baek SH, Ahn SY, Cho JH, Kim DK, Ryu DR, Kim S, Lim CS, Lee JP. Predicting outcomes of continuous renal replacement therapy using body composition monitoring: a deep-learning approach. Sci Rep 2023; 13:4605. [PMID: 36944678 PMCID: PMC10030803 DOI: 10.1038/s41598-023-30074-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 02/15/2023] [Indexed: 03/23/2023] Open
Abstract
Fluid balance is a critical prognostic factor for patients with severe acute kidney injury (AKI) requiring continuous renal replacement therapy (CRRT). This study evaluated whether repeated fluid balance monitoring could improve prognosis in this clinical population. This was a multicenter retrospective study that included 784 patients (mean age, 67.8 years; males, 66.4%) with severe AKI requiring CRRT during 2017-2019 who were treated in eight tertiary hospitals in Korea. Sequential changes in total body water were compared between patients who died (event group) and those who survived (control group) using mixed-effects linear regression analyses. The performance of various machine learning methods, including recurrent neural networks, was compared to that of existing prognostic clinical scores. After adjusting for confounding factors, a marginal benefit of fluid balance was identified for the control group compared to that for the event group (p = 0.074). The deep-learning model using a recurrent neural network with an autoencoder and including fluid balance monitoring provided the best differentiation between the groups (area under the curve, 0.793) compared to 0.604 and 0.606 for SOFA and APACHE II scores, respectively. Our prognostic, deep-learning model underlines the importance of fluid balance monitoring for prognosis assessment among patients receiving CRRT.
Collapse
Affiliation(s)
- Kyung Don Yoo
- Division of Nephrology, Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea
| | - Junhyug Noh
- Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Wonho Bae
- University of British Columbia, Vancouver, Canada
| | - Jung Nam An
- Division of Nephrology, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
| | - Hyung Jung Oh
- Division of Nephrology, Department of Internal Medicine, Sheikh Khalifa Specialty Hospital, Ra's al Khaimah, United Arab Emirates
| | - Harin Rhee
- Division of Nephrology, Department of Internal Medicine, Pusan National University Hospital, Busan, Republic of Korea
| | - Eun Young Seong
- Division of Nephrology, Department of Internal Medicine, Pusan National University Hospital, Busan, Republic of Korea
| | - Seon Ha Baek
- Division of Nephrology, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Republic of Korea
| | - Shin Young Ahn
- Division of Nephrology, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Jang-Hee Cho
- Division of Nephrology, Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Dong Ki Kim
- Division of Nephrology, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Kidney Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Dong-Ryeol Ryu
- Division of Nephrology, Department of Internal Medicine, School of Medicine, Ehwa Womans University, Seoul, Republic of Korea
| | - Sejoong Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Division of Nephrology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Chun Soo Lim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Kidney Research Institute, Seoul National University College of Medicine, Seoul, Korea
- Division of Nephrology, Department of Internal Medicine, Seoul National University Boramae Medical Center, 20 Boramae-Ro 5-Gil, Dongjak-gu, Seoul, 156-707, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
- Kidney Research Institute, Seoul National University College of Medicine, Seoul, Korea.
- Division of Nephrology, Department of Internal Medicine, Seoul National University Boramae Medical Center, 20 Boramae-Ro 5-Gil, Dongjak-gu, Seoul, 156-707, Republic of Korea.
| | | |
Collapse
|
15
|
Hsu CT, Pai KC, Chen LC, Lin SH, Wu MJ. Machine Learning Models to Predict the Risk of Rapidly Progressive Kidney Disease and the Need for Nephrology Referral in Adult Patients with Type 2 Diabetes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3396. [PMID: 36834088 PMCID: PMC9967274 DOI: 10.3390/ijerph20043396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/10/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Early detection of rapidly progressive kidney disease is key to improving the renal outcome and reducing complications in adult patients with type 2 diabetes mellitus (T2DM). We aimed to construct a 6-month machine learning (ML) predictive model for the risk of rapidly progressive kidney disease and the need for nephrology referral in adult patients with T2DM and an initial estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m2. We extracted patients and medical features from the electronic medical records (EMR), and the cohort was divided into a training/validation and testing data set to develop and validate the models on the basis of three algorithms: logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost). We also applied an ensemble approach using soft voting classifier to classify the referral group. We used the area under the receiver operating characteristic curve (AUROC), precision, recall, and accuracy as the metrics to evaluate the performance. Shapley additive explanations (SHAP) values were used to evaluate the feature importance. The XGB model had higher accuracy and relatively higher precision in the referral group as compared with the LR and RF models, but LR and RF models had higher recall in the referral group. In general, the ensemble voting classifier had relatively higher accuracy, higher AUROC, and higher recall in the referral group as compared with the other three models. In addition, we found a more specific definition of the target improved the model performance in our study. In conclusion, we built a 6-month ML predictive model for the risk of rapidly progressive kidney disease. Early detection and then nephrology referral may facilitate appropriate management.
Collapse
Affiliation(s)
- Chia-Tien Hsu
- Division of Nephrology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Kai-Chih Pai
- College of Engineering, Tunghai University, Taichung 407224, Taiwan
| | - Lun-Chi Chen
- College of Engineering, Tunghai University, Taichung 407224, Taiwan
| | - Shau-Hung Lin
- DDS-THU AI Center, Tunghai University, Taichung 407224, Taiwan
| | - Ming-Ju Wu
- Division of Nephrology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 40227, Taiwan
- RongHsing Research Center for Translational Medicine, College of Life Sciences, National Chung Hsing University, Taichung 40227, Taiwan
- Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung 40227, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung 404333, Taiwan
| |
Collapse
|
16
|
Alabdaljabar MS, Hasan B, Noseworthy PA, Maalouf JF, Ammash NM, Hashmi SK. Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries. J Multidiscip Healthc 2023; 16:285-295. [PMID: 36741292 PMCID: PMC9891080 DOI: 10.2147/jmdh.s383810] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/07/2022] [Indexed: 01/30/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients' lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs.
Collapse
Affiliation(s)
- Mohamad S Alabdaljabar
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA,College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Babar Hasan
- Sindh Institute of Urology and Transplantation (SIUT), Karachi, Pakistan
| | | | - Joseph F Maalouf
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA,Department of Medicine, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates
| | - Naser M Ammash
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA,Department of Medicine, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates
| | - Shahrukh K Hashmi
- Department of Medicine, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates,Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN, USA,Correspondence: Shahrukh K Hashmi, Department of Medicine, SSMC, Abu Dhabi, United Arab Emirates, Email
| |
Collapse
|
17
|
Distinct Subtypes of Hepatorenal Syndrome and Associated Outcomes as Identified by Machine Learning Consensus Clustering. Diseases 2023; 11:diseases11010018. [PMID: 36810532 PMCID: PMC9944494 DOI: 10.3390/diseases11010018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/15/2023] [Accepted: 01/20/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND The utilization of multi-dimensional patient data to subtype hepatorenal syndrome (HRS) can individualize patient care. Machine learning (ML) consensus clustering may identify HRS subgroups with unique clinical profiles. In this study, we aim to identify clinically meaningful clusters of hospitalized patients for HRS using an unsupervised ML clustering approach. METHODS Consensus clustering analysis was performed based on patient characteristics in 5564 patients primarily admitted for HRS in the National Inpatient Sample from 2003-2014 to identify clinically distinct HRS subgroups. We applied standardized mean difference to evaluate key subgroup features, and compared in-hospital mortality between assigned clusters. RESULTS The algorithm revealed four best distinct HRS subgroups based on patient characteristics. Cluster 1 patients (n = 1617) were older, and more likely to have non-alcoholic fatty liver disease, cardiovascular comorbidities, hypertension, and diabetes. Cluster 2 patients (n = 1577) were younger and more likely to have hepatitis C, and less likely to have acute liver failure. Cluster 3 patients (n = 642) were younger, and more likely to have non-elective admission, acetaminophen overdose, acute liver failure, to develop in-hospital medical complications and organ system failure, and to require supporting therapies, including renal replacement therapy, and mechanical ventilation. Cluster 4 patients (n = 1728) were younger, and more likely to have alcoholic cirrhosis and to smoke. Thirty-three percent of patients died in hospital. In-hospital mortality was higher in cluster 1 (OR 1.53; 95% CI 1.31-1.79) and cluster 3 (OR 7.03; 95% CI 5.73-8.62), compared to cluster 2, while cluster 4 had comparable in-hospital mortality (OR 1.13; 95% CI 0.97-1.32). CONCLUSIONS Consensus clustering analysis provides the pattern of clinical characteristics and clinically distinct HRS phenotypes with different outcomes.
Collapse
|
18
|
Scherer L, Kuss M, Nahm W. Review of Artificial Intelligence-Based Signal Processing in Dialysis: Challenges for Machine-Embedded and Complementary Applications. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:40-46. [PMID: 36723281 DOI: 10.1053/j.akdh.2022.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/23/2022] [Accepted: 11/07/2022] [Indexed: 01/20/2023]
Abstract
Artificial intelligence technology is trending in nearly every medical area. It offers the possibility for improving analytics, therapy outcome, and user experience during therapy. In dialysis, the application of artificial intelligence as a therapy-individualization tool is led more by start-ups than consolidated players, and innovation in dialysis seems comparably stagnant. Factors such as technical requirements or regulatory processes are important and necessary but can slow down the implementation of artificial intelligence due to missing data infrastructure and undefined approval processes. Current research focuses mainly on analyzing health records or wearable technology to add to existing health data. It barely uses signal data from treatment devices to apply artificial intelligence models. This article, therefore, discusses requirements for signal processing through artificial intelligence in health care and compares these with the status quo in dialysis therapy. It offers solutions for given barriers to speed up innovation with sensor data, opening access to existing and untapped sources, and shows the unique advantage of signal processing in dialysis compared to other health care domains. This research shows that even though the combination of different data is vital for improving patients' therapy, adding signal-based treatment data from dialysis devices to the picture can benefit the understanding of treatment dynamics, improving and individualizing therapy.
Collapse
Affiliation(s)
- Lena Scherer
- Karlsruhe Institute of Technology, Karlsruhe, Germany.
| | | | - Werner Nahm
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| |
Collapse
|
19
|
Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare (Basel) 2022; 10:healthcare10122493. [PMID: 36554017 PMCID: PMC9777836 DOI: 10.3390/healthcare10122493] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/03/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.
Collapse
|
20
|
Magherini R, Mussi E, Volpe Y, Furferi R, Buonamici F, Servi M. Machine Learning for Renal Pathologies: An Updated Survey. SENSORS 2022; 22:s22134989. [PMID: 35808481 PMCID: PMC9269842 DOI: 10.3390/s22134989] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 12/04/2022]
Abstract
Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.
Collapse
|
21
|
Liang Q, Xu Y, Zhou Y, Chen X, Chen J, Huang M. Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study. BMJ Open 2022; 12:e054092. [PMID: 35241466 PMCID: PMC8896056 DOI: 10.1136/bmjopen-2021-054092] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVES There are many studies of acute kidney injury (AKI) diagnosis models lack of external validation and prospective validation. We constructed the models using three databases to predict severe AKI within 48 hours in intensive care unit (ICU) patients. DESIGN A retrospective and prospective cohort study. SETTING We studied critically ill patients in our database (SHZJU-ICU) and two other public databases, the Medical Information Mart for Intensive Care (MIMIC) and AmsterdamUMC databases, including basic demographics, vital signs and laboratory results. We predicted the diagnosis of severe AKI in patients in the next 48 hours using machine-learning algorithms with the three databases. Then, we carried out real-time severe AKI prediction in the prospective validation study at our centre for 1 year. PARTICIPANTS All patients included in three databases with uniform exclusion criteria. PRIMARY AND SECONDARY OUTCOME MEASURES Effect evaluation index of prediction models. RESULTS We included 58 492 patients, and a total of 5257 (9.0%) patients met the definition of severe AKI. In the internal validation of the SHZJU-ICU and MIMIC databases, the best area under the receiver operating characteristic curve (AUROC) of the model was 0.86. The external validation results by AmsterdamUMC database were also satisfactory, with the best AUROC of 0.86. A total of 2532 patients were admitted to the centre for prospective validation; 358 positive results were predicted and 344 patients were diagnosed with severe AKI, with the best sensitivity of 0.72, the specificity of 0.80 and the AUROC of 0.84. CONCLUSION The prediction model of severe AKI exhibits promises as a clinical application based on dynamic vital signs and laboratory results of multicentre databases with prospective and external validation.
Collapse
Affiliation(s)
- Qiqiang Liang
- General intensive care unit, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Yongfeng Xu
- General intensive care unit, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Yu Zhou
- General intensive care unit, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Xinyi Chen
- General intensive care unit, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Juan Chen
- General intensive care unit, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Man Huang
- General intensive care unit, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| |
Collapse
|
22
|
Chien TY, Ting HW, Chen CF, Yang CZ, Chen CY. A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center. Int J Med Sci 2022; 19:1049-1055. [PMID: 35813300 PMCID: PMC9254376 DOI: 10.7150/ijms.71341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 05/19/2022] [Indexed: 11/05/2022] Open
Abstract
Background: Diabetes mellitus (DM) is a major public health problem worldwide. It involves dysfunction of blood sugar regulation resulting from insulin resistance, inadequate insulin secretion, or excessive glucagon secretion. Methods: This study collated 971,401 drug usage records of 51,009 DM patients. These data include patient identification code, age, gender, outpatient visiting dates, visiting code, medication features (included items, doses, and frequencies of drugs), HbA1c results, and testing time. We apply a random forest (RF) model for feature selection and implement a regression model with the bidirectional long short-term memory (Bi-LSTM) deep learning architecture. Finally, we use the root mean square error (RMSE) as the evaluation index for the prediction model. Results: After data cleaning, the data included 8,729 male and 9,115 female cases. Metformin was the most important feature suggested by the RF model, followed by glimepiride, acarbose, pioglitazone, glibenclamide, gliclazide, repaglinide, nateglinide, sitagliptin, and vildagliptin. The model performed better with the past two seasons in the training data than with additional seasons. Further, the Bi-LSTM architecture model performed better than support vector machines (SVMs). Discussion & Conclusion: This study found that Bi-LSTM models is a well kernel in a CDSS which help physicians' decision-making, and the increasing the number of seasons will negative impact the performance. In addition, this study found that the most important drug is metformin, which is recommended as first-line treatment OHA in various situations for DM patients.
Collapse
Affiliation(s)
- Ting-Ying Chien
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan.,Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan City, Taiwan.,Graduated Program in Biomedical Informatics, Yuan Ze University, Taoyuan City, Taiwan
| | - Hsien-Wei Ting
- Graduated Program in Biomedical Informatics, Yuan Ze University, Taoyuan City, Taiwan.,Department of Neurosurgery, Taipei Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan
| | - Chih-Fang Chen
- Department of Pharmacy, MacKay Memorial Hospital, Taipei City, Taiwan
| | - Cheng-Zen Yang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan.,Graduated Program in Biomedical Informatics, Yuan Ze University, Taoyuan City, Taiwan
| | - Chong-Yi Chen
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan
| |
Collapse
|
23
|
Kidney Cancer and Chronic Kidney Disease: Too Close for Comfort. Biomedicines 2021; 9:biomedicines9121761. [PMID: 34944574 PMCID: PMC8699019 DOI: 10.3390/biomedicines9121761] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/18/2021] [Accepted: 11/21/2021] [Indexed: 12/11/2022] Open
Abstract
Kidney cancer and chronic kidney disease are two renal pathologies with very different clinical management strategies and therapeutical options. Nonetheless, the cellular and molecular mechanisms underlying both conditions are closely related. Renal physiology is adapted to operate with a limited oxygen supply, making the kidney remarkably equipped to respond to hypoxia. This tightly regulated response mechanism is at the heart of kidney cancer, leading to the onset of malignant cellular phenotypes. Although elusive, the role of hypoxia in chronic kidney diseases is emerging as related to fibrosis, a pivotal factor in decaying renal function. The present review offers a perspective on the common biological traits shared between kidney cancer and chronic kidney disease and the available and prospective therapies for both conditions.
Collapse
|
24
|
Lindenmeyer MT, Alakwaa F, Rose M, Kretzler M. Perspectives in systems nephrology. Cell Tissue Res 2021; 385:475-488. [PMID: 34027630 PMCID: PMC8523456 DOI: 10.1007/s00441-021-03470-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 04/28/2021] [Indexed: 12/19/2022]
Abstract
Chronic kidney diseases (CKD) are a major health problem affecting approximately 10% of the world’s population and posing increasing challenges to the healthcare system. While CKD encompasses a broad spectrum of pathological processes and diverse etiologies, the classification of kidney disease is currently based on clinical findings or histopathological categorizations. This descriptive classification is agnostic towards the underlying disease mechanisms and has limited progress towards the ability to predict disease prognosis and treatment responses. To gain better insight into the complex and heterogeneous disease pathophysiology of CKD, a systems biology approach can be transformative. Rather than examining one factor or pathway at a time, as in the reductionist approach, with this strategy a broad spectrum of information is integrated, including comprehensive multi-omics data, clinical phenotypic information, and clinicopathological parameters. In recent years, rapid advances in mathematical, statistical, computational, and artificial intelligence methods enable the mapping of diverse big data sets. This holistic approach aims to identify the molecular basis of CKD subtypes as well as individual determinants of disease manifestation in a given patient. The emerging mechanism-based patient stratification and disease classification will lead to improved prognostic and predictive diagnostics and the discovery of novel molecular disease-specific therapies.
Collapse
Affiliation(s)
- Maja T Lindenmeyer
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Fadhl Alakwaa
- Department of Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Michael Rose
- Department of Medicine, University of Michigan, Ann Arbor, MI, USA
| | | |
Collapse
|
25
|
Ho CWL, Caals K. A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology. Semin Nephrol 2021; 41:282-293. [PMID: 34330368 DOI: 10.1016/j.semnephrol.2021.05.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Digitalization in nephrology has progressed in a manner that is disparate and siloed, even though learning (under a broader Learning Health System initiative) has been manifested in all the main areas of clinical application. Most applications based on artificial intelligence/machine learning (AI/ML) are still in the initial developmental stages and are yet to be adequately validated and shown to contribute to positive patient outcomes. There is also no consistent or comprehensive digitalization plan, and insufficient data are a limiting factor across all of these areas. In this article, we first consider how digitalization along nephrology care pathways relates to the Learning Health System initiative. We then consider the current state of AI/ML-based software and devices in nephrology and the ethical and regulatory challenges in scaling them up toward broader clinical application. We conclude with our proposal to establish a dedicated ethics and governance framework that is centered around health care providers in nephrology and the AI/ML-based software to which their work relates. This framework should help to integrate ethical and regulatory values and considerations, involve a wide range of stakeholders, and apply across normative domains that are conventionally demarcated as clinical, research, and public health.
Collapse
Affiliation(s)
- Calvin Wai-Loon Ho
- Centre for Medical Ethics and Law, Department of Law, The University of Hong Kong, Hong Kong SAR.
| | - Karel Caals
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| |
Collapse
|
26
|
Park S, Park BS, Lee YJ, Kim IH, Park JH, Ko J, Kim YW, Park KM. Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR. Medicine (Baltimore) 2021; 100:e25422. [PMID: 33832141 PMCID: PMC8036048 DOI: 10.1097/md.0000000000025422] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 02/27/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has had a significant impact on our lives and plays many roles in various fields. By analyzing the past 30 years of AI trends in the field of nephrology, using a bibliography, we wanted to know the areas of interest and future direction of AI in research related to the kidney. METHODS Using the Institute for Scientific Information Web of Knowledge database, we searched for articles published from 1990 to 2019 in January 2020 using the keywords AI; deep learning; machine learning; and kidney (or renal). The selected articles were reviewed manually at the points of citation analysis. RESULTS From 218 related articles, we selected the top fifty with 1188 citations in total. The most-cited article was cited 84 times and the least-cited one was cited 12 times. These articles were published in 40 journals. Expert Systems with Applications (three articles) and Kidney International (three articles) were the most cited journals. Forty articles were published in the 2010s, and seven articles were published in the 2000s. The top-fifty most cited articles originated from 17 countries; the USA contributed 16 articles, followed by Turkey with four articles. The main topics in the top fifty consisted of tumors (11), acute kidney injury (10), dialysis-related (5), kidney-transplant related (4), nephrotoxicity (4), glomerular disease (4), chronic kidney disease (3), polycystic kidney disease (2), kidney stone (2), kidney image (2), renal pathology (2), and glomerular filtration rate measure (1). CONCLUSIONS After 2010, the interest in AI and its achievements increased enormously. To date, AIs have been investigated using data that are relatively easy to access, for example, radiologic images and laboratory results in the fields of tumor and acute kidney injury. In the near future, a deeper and wider range of information, such as genetic and personalized database, will help enrich nephrology fields with AI technology.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Kang Min Park
- Department of Neurology, Inje University Haeundae Paik Hospital, Busan, Korea
| |
Collapse
|
27
|
Chang JF, Chen PC, Hsieh CY, Liou JC. A Growth Differentiation Factor 15-Based Risk Score Model to Predict Mortality in Hemodialysis Patients. Diagnostics (Basel) 2021; 11:diagnostics11020286. [PMID: 33670413 PMCID: PMC7918408 DOI: 10.3390/diagnostics11020286] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/22/2021] [Accepted: 02/07/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The risk of cardiovascular (CV) and fatal events remains extremely high in patients with maintenance hemodialysis (MHD), and the growth differentiation factor 15 (GDF15) has emerged as a valid risk stratification biomarker. We aimed to develop a GDF15-based risk score as a death prediction model for MHD patients. METHODS Age, biomarker levels, and clinical parameters were evaluated at study entry. One hundred and seventy patients with complete information were finally included for data analysis. We performed the Cox regression analysis of various prognostic factors for mortality. Then, age, GDF15, and robust clinical predictors were included as a risk score model to assess the predictive accuracy for all-cause and CV death in the receiver operating characteristic (ROC) curve analysis. RESULTS Age, GDF15, and albumin were significantly associated with higher all-cause and CV mortality risk that were combined as a risk score model. The highest tertile of GDF-15 (>1707.1 pg/mL) was associated with all-cause mortality (adjusted hazard ratios (aHRs): 3.06 (95% confidence interval (CI): 1.20-7.82), p < 0.05) and CV mortality (aHRs: 3.11 (95% CI: 1.02-9.50), p < 0.05). The ROC analysis of GDF-15 tertiles for all-cause and CV mortality showed 0.68 (95% CI = 0.59 to 0.77) and 0.68 (95% CI = 0.58 to 0.79), respectively. By contrast, the GDF15-based prediction model for all-cause and CV mortality showed 0.75 (95% CI: 0.67-0.82) and 0.72 (95% CI: 0.63-0.81), respectively. CONCLUSION Age, GDF15, and hypoalbuminemia predict all-cause and CV death in MHD patients, yet a combination scoring system provides more robust predictive powers. An elevated GDF15-based risk score warns clinicians to determine an appropriate intervention in advance. In light of this, the GDF15-based death prediction model could be developed in the artificial intelligence-based precision medicine.
Collapse
Affiliation(s)
- Jia-Feng Chang
- Division of Nephrology, Department of Internal Medicine, En Chu Kong Hospital, New Taipei City 237, Taiwan; (J.-F.C.); (C.-Y.H.)
- Department of Nursing, Yuanpei University of Medical Technology, Hsinchu 300, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235, Taiwan
- Graduate Institute of Aerospace and Undersea Medicine, Department of Medicine, National Defense Medical Center, Taipei 114, Taiwan
- Renal Care Joint Foundation, New Taipei City 220, Taiwan
| | - Po-Cheng Chen
- Department of Urology, En Chu Kong Hospital, New Taipei City 237, Taiwan;
| | - Chih-Yu Hsieh
- Division of Nephrology, Department of Internal Medicine, En Chu Kong Hospital, New Taipei City 237, Taiwan; (J.-F.C.); (C.-Y.H.)
- School of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
| | - Jian-Chiun Liou
- School of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
- Correspondence:
| |
Collapse
|
28
|
Kang EYC, Hsieh YT, Li CH, Huang YJ, Kuo CF, Kang JH, Chen KJ, Lai CC, Wu WC, Hwang YS. Deep Learning-Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation. JMIR Med Inform 2020; 8:e23472. [PMID: 33139242 PMCID: PMC7728538 DOI: 10.2196/23472] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/01/2020] [Accepted: 10/30/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Retinal imaging has been applied for detecting eye diseases and cardiovascular risks using deep learning-based methods. Furthermore, retinal microvascular and structural changes were found in renal function impairments. However, a deep learning-based method using retinal images for detecting early renal function impairment has not yet been well studied. OBJECTIVE This study aimed to develop and evaluate a deep learning model for detecting early renal function impairment using retinal fundus images. METHODS This retrospective study enrolled patients who underwent renal function tests with color fundus images captured at any time between January 1, 2001, and August 31, 2019. A deep learning model was constructed to detect impaired renal function from the images. Early renal function impairment was defined as estimated glomerular filtration rate <90 mL/min/1.73 m2. Model performance was evaluated with respect to the receiver operating characteristic curve and area under the curve (AUC). RESULTS In total, 25,706 retinal fundus images were obtained from 6212 patients for the study period. The images were divided at an 8:1:1 ratio. The training, validation, and testing data sets respectively contained 20,787, 2189, and 2730 images from 4970, 621, and 621 patients. There were 10,686 and 15,020 images determined to indicate normal and impaired renal function, respectively. The AUC of the model was 0.81 in the overall population. In subgroups stratified by serum hemoglobin A1c (HbA1c) level, the AUCs were 0.81, 0.84, 0.85, and 0.87 for the HbA1c levels of ≤6.5%, >6.5%, >7.5%, and >10%, respectively. CONCLUSIONS The deep learning model in this study enables the detection of early renal function impairment using retinal fundus images. The model was more accurate for patients with elevated serum HbA1c levels.
Collapse
Affiliation(s)
- Eugene Yu-Chuan Kang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yi-Ting Hsieh
- Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan
| | | | - Yi-Jin Huang
- Acer Healthcare Incorporated, New Taipei, Taiwan
| | - Chang-Fu Kuo
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Je-Ho Kang
- Department of Nephrology, Yang Ming Hospital, Taoyuan, Taiwan
| | - Kuan-Jen Chen
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chi-Chun Lai
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Wei-Chi Wu
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yih-Shiou Hwang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| |
Collapse
|
29
|
Thongprayoon C, Hansrivijit P, Bathini T, Vallabhajosyula S, Mekraksakit P, Kaewput W, Cheungpasitporn W. Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches. J Clin Med 2020; 9:jcm9061767. [PMID: 32517295 PMCID: PMC7355827 DOI: 10.3390/jcm9061767] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 06/04/2020] [Indexed: 02/08/2023] Open
Abstract
Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.
Collapse
Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85724, USA;
| | | | - Poemlarp Mekraksakit
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79424, USA;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
| |
Collapse
|