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Sun R, Li S, Wei Y, Hu L, Xu Q, Zhan G, Yan X, He Y, Wang Y, Li X, Luo A, Zhou Z. Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study. Int J Surg 2024; 110:2950-2962. [PMID: 38445452 DOI: 10.1097/js9.0000000000001237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors. MATERIALS AND METHODS Adult patients undergoing noncardiac surgery were retrospectively included in the study (76 457 patients in the discovery cohort and 11 910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. RESULTS The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835-0.863) and 0.828 (95% CI: 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models' predictive performance. CONCLUSIONS The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.
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Affiliation(s)
- Rao Sun
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Shiyong Li
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Yuna Wei
- Yidu Cloud Technology Inc, Beijing, People's Republic of China
| | - Liu Hu
- Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei
| | - Qiaoqiao Xu
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Gaofeng Zhan
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Xu Yan
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Yuqin He
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Yao Wang
- Yidu Cloud Technology Inc, Beijing, People's Republic of China
| | - Xinhua Li
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Ailin Luo
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Zhiqiang Zhou
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
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Musiał K, Stojanowski J, Augustynowicz M, Miśkiewicz-Migoń I, Kałwak K, Ussowicz M. Assessment of Risk Factors for Acute Kidney Injury with Machine Learning Tools in Children Undergoing Hematopoietic Stem Cell Transplantation. J Clin Med 2024; 13:2266. [PMID: 38673539 PMCID: PMC11050842 DOI: 10.3390/jcm13082266] [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: 02/28/2024] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Background: Although acute kidney injury (AKI) is a common complication in patients undergoing hematopoietic stem cell transplantation (HSCT), its prophylaxis remains a clinical challenge. Attempts at prevention or early diagnosis focus on various methods for the identification of factors influencing the incidence of AKI. Our aim was to test the artificial intelligence (AI) potential in the construction of a model defining parameters predicting AKI development. Methods: The analysis covered the clinical data of children followed up for 6 months after HSCT. Kidney function was assessed before conditioning therapy, 24 h after HSCT, 1, 2, 3, 4, and 8 weeks after transplantation, and, finally, 3 and 6 months post-transplant. The type of donor, conditioning protocol, and complications were incorporated into the model. Results: A random forest classifier (RFC) labeled the 93 patients according to presence or absence of AKI. The RFC model revealed that the values of the estimated glomerular filtration rate (eGFR) before and just after HSCT, as well as methotrexate use, acute graft versus host disease (GvHD), and viral infection occurrence, were the major determinants of AKI incidence within the 6-month post-transplant observation period. Conclusions: Artificial intelligence seems a promising tool in predicting the potential risk of developing AKI, even before HSCT or just after the procedure.
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Affiliation(s)
- Kinga Musiał
- Department of Pediatric Nephrology, Wrocław Medical University, Borowska 213, 50-556 Wrocław, Poland
| | - Jakub Stojanowski
- Department of Nephrology and Transplantation Medicine, Wrocław Medical University, 50-556 Wrocław, Poland;
| | - Monika Augustynowicz
- Clinic of Pediatric Nephrology, University Clinical Hospital, Borowska 213, 50-556 Wroclaw, Poland
| | - Izabella Miśkiewicz-Migoń
- Clinical Department of Pediatric Oncology and Hematology, Mother and Child Health Center, Karol Marcinkowski University Hospital, 65-046 Zielona Góra, Poland
| | - Krzysztof Kałwak
- Department of Pediatric Bone Marrow Transplantation, Oncology and Hematology, Wrocław Medical University, 50-556 Wrocław, Poland; (K.K.); (M.U.)
| | - Marek Ussowicz
- Department of Pediatric Bone Marrow Transplantation, Oncology and Hematology, Wrocław Medical University, 50-556 Wrocław, Poland; (K.K.); (M.U.)
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Yin M, Lin J, Wang Y, Liu Y, Zhang R, Duan W, Zhou Z, Zhu S, Gao J, Liu L, Liu X, Gu C, Huang Z, Xu X, Xu C, Zhu J. Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning. Int J Med Inform 2024; 184:105341. [PMID: 38290243 DOI: 10.1016/j.ijmedinf.2024.105341] [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: 07/02/2023] [Revised: 12/16/2023] [Accepted: 01/14/2024] [Indexed: 02/01/2024]
Abstract
OBJECTIVE Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). METHODS In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model β was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. RESULTS A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model β: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)]. CONCLUSION The proposed multimodal model outperformed any single-modality models and traditional scoring systems.
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Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Jiaxi Lin
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Yu Wang
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Department of General Surgery, Jintan Hospital Affiliated to Jiangsu University, Changzhou, Jiangsu 213299, China
| | - Yuanjun Liu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Rufa Zhang
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Suzhou, Jiangsu 215500, China
| | - Wenbin Duan
- Department of Hepatobiliary Surgery, the People's Hospital of Hunan Province, Changsha, Hunan 410002, China
| | - Zhirun Zhou
- Department of Obstetrics and Gynaecology, the Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, China
| | - Shiqi Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Jingwen Gao
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Lu Liu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Xiaolin Liu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Chenqi Gu
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China
| | - Zhou Huang
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China
| | - Xiaodan Xu
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Suzhou, Jiangsu 215500, China.
| | - Chunfang Xu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China; Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150000, China.
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Jia T, Xu K, Bai Y, Lv M, Shan L, Li W, Zhang X, Li Z, Wang Z, Zhao X, Li M, Zhang Y. Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study. BMC Med Inform Decis Mak 2023; 23:270. [PMID: 37996844 PMCID: PMC10668365 DOI: 10.1186/s12911-023-02376-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Acute kidney injury (AKI) after coronary artery bypass grafting (CABG) surgery is associated with poor outcomes. The objective of this study was to apply a new machine learning (ML) method to establish prediction models of AKI after CABG. METHODS A total of 2,780 patients from two medical centers in East China who underwent primary isolated CABG were enrolled. The dataset was randomly divided for model training (80%) and model testing (20%). Four ML models based on LightGBM, Support vector machine (SVM), Softmax and random forest (RF) algorithms respectively were established in Python. A total of 2,051 patients from two other medical centers were assigned to an external validation group to verify the performances of the ML prediction models. The models were evaluated using the area under the receiver operating characteristics curve (AUC), Hosmer-Lemeshow goodness-of-fit statistic, Bland-Altman plots, and decision curve analysis. The outcome of the LightGBM model was interpreted using SHapley Additive exPlanations (SHAP). RESULTS The incidence of postoperative AKI in the modeling group was 13.4%. Similarly, the incidence of postoperative AKI of the two medical centers in the external validation group was 8.2% and 13.6% respectively. LightGBM performed the best in predicting, with an AUC of 0.8027 in internal validation group and 0.8798 and 0.7801 in the external validation group. The SHAP revealed the top 20 predictors of postoperative AKI ranked according to the importance, and the top three features on prediction were the serum creatinine in the first 24 h after operation, the last preoperative Scr level, and body surface area. CONCLUSION This study provides a LightGBM predictive model that can make accurate predictions for AKI after CABG surgery. The LightGBM model shows good predictive ability in both internal and external validation. It can help cardiac surgeons identify high-risk patients who may experience AKI after CABG surgery.
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Affiliation(s)
- Tianchen Jia
- College of Information Science, Shanghai Ocean University, Shanghai, P.R. China
| | - Kai Xu
- Department of Cardiovascular Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China
| | - Yun Bai
- College of Information Science, Shanghai Ocean University, Shanghai, P.R. China
| | - Mengwei Lv
- Department of Thoracic Surgery, Xuzhou Cancer Hospital, Xuzhou, P.R. China
| | - Lingtong Shan
- Department of Thoracic Surgery, Sheyang County People's Hospital, Yancheng, P.R. China
| | - Wei Li
- Department of Cardiovascular Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, 241 Huaihai West Road, Shanghai, 200120, China
| | - Xiaobin Zhang
- Department of Cardiovascular Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, 241 Huaihai West Road, Shanghai, 200120, China
| | - Zhi Li
- Department of Cardiovascular Surgery, Jiangsu Province Hospital, the First Affiliated Hospital of Nanjing Medical University, Nanjing, P.R. China
| | - Zhenhua Wang
- College of Information Science, Shanghai Ocean University, Shanghai, P.R. China
| | - Xin Zhao
- Department of Cardiovascular Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China.
| | - Mingliang Li
- Department of Cardiovascular Surgery, The General Hospital of Ningxia Medical University, Yinchuan, Ningxia, P.R. China.
| | - Yangyang Zhang
- Department of Cardiovascular Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, 241 Huaihai West Road, Shanghai, 200120, China.
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Musiał K, Stojanowski J, Miśkiewicz-Bujna J, Kałwak K, Ussowicz M. KIM-1, IL-18, and NGAL, in the Machine Learning Prediction of Kidney Injury among Children Undergoing Hematopoietic Stem Cell Transplantation-A Pilot Study. Int J Mol Sci 2023; 24:15791. [PMID: 37958774 PMCID: PMC10648899 DOI: 10.3390/ijms242115791] [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: 09/27/2023] [Revised: 10/26/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
Children undergoing allogeneic hematopoietic stem cell transplantation (HSCT) are prone to developing acute kidney injury (AKI). Markers of kidney damage: kidney injury molecule (KIM)-1, interleukin (IL)-18, and neutrophil gelatinase-associated lipocalin (NGAL) may ease early diagnosis of AKI. The aim of this study was to assess serum concentrations of KIM-1, IL-18, and NGAL in children undergoing HSCT in relation to classical markers of kidney function (creatinine, cystatin C, estimated glomerular filtration rate (eGFR)) and to analyze their usefulness as predictors of kidney damage with the use of artificial intelligence tools. Serum concentrations of KIM-1, IL-18, NGAL, and cystatin C were assessed by ELISA in 27 children undergoing HSCT before transplantation and up to 4 weeks after the procedure. The data was used to build a Random Forest Classifier (RFC) model of renal injury prediction. The RFC model established on the basis of 3 input variables, KIM-1, IL-18, and NGAL concentrations in the serum of children before HSCT, was able to effectively assess the rate of patients with hyperfiltration, a surrogate marker of kidney injury 4 weeks after the procedure. With the use of the RFC model, serum KIM-1, IL-18, and NGAL may serve as markers of incipient renal dysfunction in children after HSCT.
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Affiliation(s)
- Kinga Musiał
- Department of Pediatric Nephrology, Wrocław Medical University, Borowska 213, 50-556 Wrocław, Poland
| | - Jakub Stojanowski
- Department of Nephrology and Transplantation Medicine, Wrocław Medical University, 50-556 Wrocław, Poland
| | - Justyna Miśkiewicz-Bujna
- Clinical Department of Pediatric Oncology and Hematology, Mother and Child Health Center, Karol Marcinkowski University Hospital, 65-046 Zielona Góra, Poland
| | - Krzysztof Kałwak
- Department of Pediatric Bone Marrow Transplantation, Oncology and Hematology, Wrocław Medical University, 50-556 Wrocław, Poland
| | - Marek Ussowicz
- Department of Pediatric Bone Marrow Transplantation, Oncology and Hematology, Wrocław Medical University, 50-556 Wrocław, Poland
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Suppadungsuk S, Thongprayoon C, Miao J, Krisanapan P, Qureshi F, Kashani K, Cheungpasitporn W. Exploring the Potential of Chatbots in Critical Care Nephrology. MEDICINES (BASEL, SWITZERLAND) 2023; 10:58. [PMID: 37887265 PMCID: PMC10608511 DOI: 10.3390/medicines10100058] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023]
Abstract
The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots' accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.
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Affiliation(s)
- Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- 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
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Nephrology and Hypertension, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Kianoush Kashani
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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Krisanapan P, Tangpanithandee S, Thongprayoon C, Pattharanitima P, Cheungpasitporn W. Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence. J Clin Med 2023; 12:jcm12083018. [PMID: 37109354 PMCID: PMC10143586 DOI: 10.3390/jcm12083018] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 04/20/2023] [Indexed: 04/29/2023] Open
Abstract
Chronic kidney disease (CKD) poses a significant public health challenge, affecting approximately 11% to 13% of the global population [...].
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Affiliation(s)
- Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
- Division of Nephrology, Department of Internal Medicine, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- 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
| | - Pattharawin Pattharanitima
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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