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Zhao W, Li X, Gao L, Ai Z, Lu Y, Li J, Wang D, Li X, Song N, Huang X, Tong ZH. Machine learning-based model for predicting all-cause mortality in severe pneumonia. BMJ Open Respir Res 2025; 12:e001983. [PMID: 40122535 PMCID: PMC11934410 DOI: 10.1136/bmjresp-2023-001983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/15/2024] [Indexed: 03/25/2025] Open
Abstract
BACKGROUND Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (APACHE-II) and Sequential Organ Failure Assessment (SOFA), have limited ability to help clinicians in classification and management decisions. The goal of this study was to analyse the clinical characteristics of severe pneumonia and develop a machine learning-based mortality-prediction model for patients with severe pneumonia. METHODS Consecutive patients with severe pneumonia between 2013 and 2022 admitted to Beijing Chaoyang Hospital affiliated with Capital Medical University were included. In-hospital all-cause mortality was the outcome of this study. We performed a retrospective analysis of the cohort, stratifying patients into survival and non-survival groups, using mainstream machine learning algorithms (light gradient boosting machine, support vector classifier and random forest). We aimed to construct a mortality-prediction model for patients with severe pneumonia based on their accessible clinical and laboratory data. The discriminative ability was evaluated using the area under the receiver operating characteristic curve (AUC). The calibration curve was used to assess the fit goodness of the model, and decision curve analysis was performed to quantify clinical utility. By means of logistic regression, independent risk factors for death in severe pneumonia were figured out to provide an important basis for clinical decision-making. RESULTS A total of 875 patients were included in the development and validation cohorts, with the in-hospital mortality rate of 14.6%. The AUC of the model in the internal validation set was 0.8779 (95% CI, 0.738 to 0.974), showing a competitive discrimination ability that outperformed those of traditional clinical scoring systems, that is, APACHE-II, SOFA, CURB-65 (confusion, urea, respiratory rate, blood pressure, age ≥65 years), Pneumonia Severity Index. The calibration curve showed that the in-hospital mortality in severe pneumonia predicted by the model fit reasonably with the actual hospital mortality. In addition, the decision curve showed that the net clinical benefit was positive in both training and validation sets of hospitalised patients with severe pneumonia. Based on ensemble machine learning algorithms and logistic regression technique, the level of ferritin, lactic acid, blood urea nitrogen, creatine kinase, eosinophil and the requirement of vasopressors were identified as top independent predictors of in-hospital mortality with severe pneumonia. CONCLUSION A robust clinical model for predicting the risk of in-hospital mortality after severe pneumonia was successfully developed using machine learning techniques. The performance of this model demonstrates the effectiveness of these techniques in creating accurate predictive models, and the use of this model has the potential to greatly assist patients and clinical doctors in making well-informed decisions regarding patient care.
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Affiliation(s)
- Weichao Zhao
- Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China
- Department of Respiratory Medicine, the Ninth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xuyan Li
- Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China
| | - Lianjun Gao
- Beijing Boai hospital, Department of Respiratory and Critical Care Medicine, Beijing, China
| | - Zhuang Ai
- Sinopharm Genomics Technology Co Ltd, Changzhou, Jiangsu, China
| | - Yaping Lu
- Sinopharm Genomics Technology Co Ltd, Changzhou, Jiangsu, China
| | - Jiachen Li
- Department of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Dong Wang
- Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China
| | - Xinlou Li
- Department of Medical Research, the Ninth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Nan Song
- Capital Medical University, Beijing, Beijing, China
| | - Xuan Huang
- Capital Medical University, Beijing, Beijing, China
| | - Zhao-Hui Tong
- Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China
- Capital Medical University, Beijing, Beijing, China
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Takeshita Y, Onishi M, Masuda H, Katsuhisa M, Ikuta K, Saizen Y, Fujii M, Kasamatsu M, Inaizumi N, Maeizumi Y, Kishino Y, Nakajima T, Koujiya E, Yamakawa M, Takami Y, Yamamoto K, Umeda-Kameyama Y, Satake S, Umegaki H, Takeya Y. Machine Learning Prediction for Postdischarge Falls in Older Adults: A Multicenter Prospective Study. J Am Med Dir Assoc 2025; 26:105414. [PMID: 39701553 DOI: 10.1016/j.jamda.2024.105414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2024] [Revised: 10/30/2024] [Accepted: 11/06/2024] [Indexed: 12/21/2024]
Abstract
OBJECTIVES The study aimed to develop a machine learning (ML) model to predict early postdischarge falls in older adults using data that are easy to collect in acute care hospitals. This may reduce the burden imposed by complex measures on patients and health care staff. DESIGN This prospective multicenter study included patients admitted to and discharged from geriatric wards at 3 university hospitals and 1 national medical center in Japan between October 2019 and July 2023. SETTING AND PARTICIPANTS The participants were individuals aged ≥65 years. Of the 1307 individuals enrolled during the study period, 684 were excluded, leaving 706 for inclusion in the analysis. METHODS We extracted 19 variables from admission and discharge data, including physical, mental, psychological, and social aspects and in-hospital events, to assess the main outcome measure: falls occurring within 3 months postdischarge. We developed a prediction model using 4 major classifiers, Extra Trees, Bernoulli Naive Bayes, AdaBoost, and Random Forest, which were evaluated using a 5-fold cross-validation. The area under the receiver operating characteristic curve (AUC) was used to evaluate predictive performance. RESULTS Among the 706 patients, 114 (16.1%) reported a fall within 3 months postdischarge. The Extra Trees classifier demonstrated the best predictive performance, with an AUC of 0.73 on the test data. Important features included the Lawton Instrumental Activities of Daily Living scale, Clinical Frailty Scale (≥4 points), presence of urinary incontinence, 15-item Geriatric Depression Scale (≥5 points), and preadmission residence, all assessed at admission. CONCLUSIONS AND IMPLICATIONS To our knowledge, this is the first study to develop an ML model for predicting early postdischarge falls among older patients in acute care hospitals. The findings suggest that this model could assist in developing fall-prevention strategies to ensure seamless transition of care from hospitals to communities.
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Affiliation(s)
- Yuko Takeshita
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Mai Onishi
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hirotada Masuda
- Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Mizuki Katsuhisa
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kasumi Ikuta
- Tokyo Medical and Dental University Graduate School of Health Care Sciences, Tokyo, Japan
| | - Yuichiro Saizen
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Misaki Fujii
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Misaki Kasamatsu
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Nobuyuki Inaizumi
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuzuki Maeizumi
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yoshinobu Kishino
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Tsuneo Nakajima
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Eriko Koujiya
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Miyae Yamakawa
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan; The Japan Centre for Evidence-Based Practice: A JBI Centre of Excellence, Osaka, Japan
| | - Yoichi Takami
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Koichi Yamamoto
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | | | - Shosuke Satake
- Department of Geriatric Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Hiroyuki Umegaki
- Department of Community Healthcare and Geriatrics, Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Yasushi Takeya
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan.
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Kotsifa E, Mavroeidis VK. Present and Future Applications of Artificial Intelligence in Kidney Transplantation. J Clin Med 2024; 13:5939. [PMID: 39407999 PMCID: PMC11478249 DOI: 10.3390/jcm13195939] [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: 09/03/2024] [Revised: 09/27/2024] [Accepted: 10/02/2024] [Indexed: 10/15/2024] Open
Abstract
Artificial intelligence (AI) has a wide and increasing range of applications across various sectors. In medicine, AI has already made an impact in numerous fields, rapidly transforming healthcare delivery through its growing applications in diagnosis, treatment and overall patient care. Equally, AI is swiftly and essentially transforming the landscape of kidney transplantation (KT), offering innovative solutions for longstanding problems that have eluded resolution through traditional approaches outside its spectrum. The purpose of this review is to explore the present and future applications of artificial intelligence in KT, with a focus on pre-transplant evaluation, surgical assistance, outcomes and post-transplant care. We discuss its great potential and the inevitable limitations that accompany these technologies. We conclude that by fostering collaboration between AI technologies and medical practitioners, we can pave the way for a future where advanced, personalised care becomes the standard in KT and beyond.
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Affiliation(s)
- Evgenia Kotsifa
- Second Propaedeutic Department of Surgery, National and Kapodistrian University of Athens, General Hospital of Athens “Laiko”, Agiou Thoma 17, 157 72 Athens, Greece
| | - Vasileios K. Mavroeidis
- Department of Transplant Surgery, North Bristol NHS Trust, Southmead Hospital, Bristol BS10 5NB, UK
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Alowidi N, Ali R, Sadaqah M, Naemi FMA. Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor-Recipient Matching. Diagnostics (Basel) 2024; 14:2119. [PMID: 39410523 PMCID: PMC11475881 DOI: 10.3390/diagnostics14192119] [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: 05/30/2024] [Revised: 07/07/2024] [Accepted: 07/19/2024] [Indexed: 10/20/2024] Open
Abstract
(1) Background: Globally, the kidney donor shortage has made the allocation process critical for patients awaiting a kidney transplant. Adopting Machine Learning (ML) models for donor-recipient matching can potentially improve kidney allocation processes when compared with traditional points-based systems. (2) Methods: This study developed an ML-based approach for donor-recipient matching. A comprehensive evaluation was conducted using ten widely used classifiers (logistic regression, decision tree, random forest, support vector machine, gradient boosting, boost, CatBoost, LightGBM, naive Bayes, and neural networks) across three experimental scenarios to ensure a robust approach. The first scenario used the original dataset, the second used a merged version of the dataset, and the last scenario used a hierarchical architecture model. Additionally, a custom ranking algorithm was designed to identify the most suitable recipients. Finally, the ML-based donor-recipient matching model was integrated into a web-based platform called Nephron. (3) Results: The gradient boost model was the top performer, achieving a remarkable and consistent accuracy rate of 98% across the three experimental scenarios. Furthermore, the custom ranking algorithm outperformed the conventional cosine and Jaccard similarity methods in identifying the most suitable recipients. Importantly, the platform not only facilitated efficient patient selection and prioritisation for kidney allocation but can be flexibly adapted for other solid organ allocation systems built on similar criteria. (4) Conclusions: This study proposes an ML-based approach to optimize donor-recipient matching within the kidney allocation process. Successful implementation of this methodology demonstrates significant potential to enhance both efficiency and fairness in kidney transplantation.
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Affiliation(s)
- Nahed Alowidi
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Razan Ali
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Munera Sadaqah
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Fatmah M. A. Naemi
- Histocompatibility and Immunogenetics Laboratory, King Fahad General Hospital, Jeddah 21589, Saudi Arabia;
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Barbetta A, Rocque B, Sarode D, Bartlett JA, Emamaullee J. Revisiting transplant immunology through the lens of single-cell technologies. Semin Immunopathol 2023; 45:91-109. [PMID: 35980400 PMCID: PMC9386203 DOI: 10.1007/s00281-022-00958-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/06/2022] [Indexed: 11/03/2022]
Abstract
Solid organ transplantation (SOT) is the standard of care for end-stage organ disease. The most frequent complication of SOT involves allograft rejection, which may occur via T cell- and/or antibody-mediated mechanisms. Diagnosis of rejection in the clinical setting requires an invasive biopsy as there are currently no reliable biomarkers to detect rejection episodes. Likewise, it is virtually impossible to identify patients who exhibit operational tolerance and may be candidates for reduced or complete withdrawal of immunosuppression. Emerging single-cell technologies, including cytometry by time-of-flight (CyTOF), imaging mass cytometry, and single-cell RNA sequencing, represent a new opportunity for deep characterization of pathogenic immune populations involved in both allograft rejection and tolerance in clinical samples. These techniques enable examination of both individual cellular phenotypes and cell-to-cell interactions, ultimately providing new insights into the complex pathophysiology of allograft rejection. However, working with these large, highly dimensional datasets requires expertise in advanced data processing and analysis using computational biology techniques. Machine learning algorithms represent an optimal strategy to analyze and create predictive models using these complex datasets and will likely be essential for future clinical application of patient level results based on single-cell data. Herein, we review the existing literature on single-cell techniques in the context of SOT.
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Affiliation(s)
- Arianna Barbetta
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA
- University of Southern California, Los Angeles, CA, USA
| | - Brittany Rocque
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA
- University of Southern California, Los Angeles, CA, USA
| | - Deepika Sarode
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA
- University of Southern California, Los Angeles, CA, USA
| | - Johanna Ascher Bartlett
- Pediatric Gastroenterology, Hepatology and Nutrition, Children's Hospital of Los Angeles, Los Angeles, CA, USA
| | - Juliet Emamaullee
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA.
- University of Southern California, Los Angeles, CA, USA.
- Division of Hepatobiliary and Abdominal Organ Transplantation Surgery, Children's Hospital Los Angeles, Los Angeles, CA, USA.
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The Emerging Role of Implementing Machine Learning in Food Recommendation for Chronic Kidney Diseases Using Correlation Analysis. J FOOD QUALITY 2022. [DOI: 10.1155/2022/7176261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Kidneys are vital organs in the human body, and their effective functioning determines life quality. Chronic kidney illness is a kind of nephrotic syndrome in which the kidneys’ capacity to cope normally steadily deteriorates and remains asymptomatic for a long period as the disease progresses. An early CKD detection would help the patient recover faster and easier. Using an artificial intelligence system that can effectively aid in CKD detection in time and suggest the required food nutrition for its treatment and recovery would reap immense benefits for healthcare professionals as well as the patient. ML is a part of AI technology that has been used for effective medical development. This technology helps physicians in the accurate diagnosis of kidney disease and helps in effective treatment prediction by recommending required nutrition. The present research relates to the use of ML in proper kidney disease diagnosis and food recommendations for treatment accordingly. A correlation analysis has been done in this research to observe the strength of ML using the effective finding for renal malfunctioning and identifying the best food products that could help in its treatment and recovery. IBM SPSS version 26 has been used for this research. The correlation analysis has been done to observe the impact of eight independent variables that are age, gender, blood sugar, serum albumin, creatinine, potassium, bacteria, and pus secretion on the two dependent variables that are the risk of CKD occurrence and ML accuracy. The results have exposed that the autonomous values consist of a strong positive correlation with the dependent variable (
). The statistical significance values have proved that the dependent values are statistically significant (0.001). The value of ML accuracy at a 95% confidence level has been observed at 88.85%, and the CKD occurrence value is 86.95%. The results have proved that the ML algorithm detects the risk of CKD occurrence accurately in each stage via analyzing blood sugar, creatinine, and potassium levels. The result also shows that the risk of CKD enhances with an increase in age.
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Díez-Sanmartín C, Sarasa-Cabezuelo A, Andrés Belmonte A. The impact of artificial intelligence and big data on end-stage kidney disease treatments. EXPERT SYSTEMS WITH APPLICATIONS 2021; 180:115076. [DOI: 10.1016/j.eswa.2021.115076] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
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Schwantes IR, Axelrod DA. Technology-Enabled Care and Artificial Intelligence in Kidney Transplantation. CURRENT TRANSPLANTATION REPORTS 2021; 8:235-240. [PMID: 34341714 PMCID: PMC8317681 DOI: 10.1007/s40472-021-00336-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2021] [Indexed: 01/24/2023]
Abstract
Purpose of Review Artificial intelligence (AI), machine learning, and technology-enabled remote patient care have evolved rapidly and have now been incorporated into many aspects of medical care. Transplantation is fortunate to have large data sets upon which machine learning algorithms can be constructed. AI are now available to improve pretransplant management, donor selection, and post-operative management of transplant patients. Recent Findings Changes in patient and donor characteristics warrant new approaches to listing and organ acceptance practices. Machine learning has been employed to optimize donor selection to identify patients likely to benefit from transplantation of higher risk organs, increasing organ discard and reducing waitlist mortality. These models have greater precisions and predictive ability than currently employed metrics including the Kidney Donor Profile Index and the expected posttransplant survival models. After transplant, AI tools have been developed to optimize immunosuppression management, track patients adherence, and assess graft survival. Summary AI and technology-enabled management tools are now available throughout the transplant journey. Unfortunately, those are frequently not available at the point of decision (patient listing, organ acceptance, posttransplant clinic), limiting utilization. Incorporation of these tools into the EMR, the Donor Net® organ offer system, and mobile devices is vital to ensure widespread adoption.
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Affiliation(s)
- Issac R Schwantes
- Department of Surgery, Oregon Health & Science University, Portland, OR USA
| | - David A Axelrod
- Organ Transplant Center, University of Iowa, 200 Hawkins Dr, Iowa City, LA 52240 USA
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Seyahi N, Ozcan SG. Artificial intelligence and kidney transplantation. World J Transplant 2021; 11:277-289. [PMID: 34316452 PMCID: PMC8290997 DOI: 10.5500/wjt.v11.i7.277] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 05/17/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.
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Affiliation(s)
- Nurhan Seyahi
- Department of Nephrology, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
| | - Seyda Gul Ozcan
- Department of Internal Medicine, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
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Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: A retrospective observational cohort study in a university hospital in Japan. Int J Nurs Stud 2021; 119:103932. [PMID: 33975074 DOI: 10.1016/j.ijnurstu.2021.103932] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 02/23/2021] [Accepted: 03/17/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND In hospitals, nurses are responsible for pressure injury risk assessment using several kinds of risk assessment scales. However, their predictive validity is insufficient to initiate targeted preventive strategy for each patient. The use of electronic health records with machine learning technique is a promising strategy to provide automated clinical decision-making aid. OBJECTIVE The purpose of this study was to construct a predictive model for pressure injury development which included feature variables that can be collected on the first day of hospitalization by nurses who routinely input the data to electronic health records. DESIGN Retrospective observational cohort study. SETTING This study was conducted at a university hospital in Japan. PARTICIPANTS This study used electronic health records, which include entry/discharge records, basic nursing records, and pressure injury management documents (N = 75,353). METHODS The outcome measure was the pressure injuries which developed outside of an operation theatre and frequently appeared on the specific body parts at high risk of pressure injury development. We utilized four major classifiers: logistic regression, random forest, linear support vector machine, and extreme gradient boosting (XGBoost) with 5-fold cross-validation technique. The area under the receiver operating characteristic curve (AUC) was used for evaluating predictive performance. RESULTS The proportion of hospital-acquired pressure injuries was 0.52%. The receiver operating characteristic curves revealed the best predictive performance for the XGBoost model, achieving the highest sensitivity of 0.78±0.03 and AUC of 0.80±0.02 amongst four types of classifiers. Variables related to difficulty in activities of daily living, anorexia, and respiratory or cardiac disorders were extracted as important features. CONCLUSIONS Our findings suggest that routinely collected health data by nurses on the first day of patient admission have the potential to help determine high-risk patients for pressure injury development. Tweetable abstract: Machine learning models on routinely collected electronic health records data successfully predict pressure injury development during hospitalization. FUNDING This work was supported by a JSPS KAKENHI Grant-in-Aid for Exploratory Research (16K15865).
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Bülow RD, Dimitrov D, Boor P, Saez-Rodriguez J. How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade? Semin Immunopathol 2021; 43:739-752. [PMID: 33835214 PMCID: PMC8551101 DOI: 10.1007/s00281-021-00847-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 02/17/2021] [Indexed: 01/16/2023]
Abstract
IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.
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Affiliation(s)
- Roman David Bülow
- University Hospital RWTH Aachen, Institute of Pathology, Aachen, Germany
| | - Daniel Dimitrov
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
- Institute for Computational Biomedicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | - Peter Boor
- University Hospital RWTH Aachen, Institute of Pathology, Aachen, Germany.
- Department of Nephrology and Immunology, University Hospital RWTH Aachen, Aachen, Germany.
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany.
- Institute for Computational Biomedicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany.
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), 52074, RWTH Aachen University, Aachen, Germany.
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory and Heidelberg University, Heidelberg, Germany.
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Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence. TRANSPLANTOLOGY 2021. [DOI: 10.3390/transplantology2020012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD). Despite this success, the high risk of premature death and frequent occurrence of graft failure remain important clinical and research challenges. The current burst of studies and other innovative initiatives using artificial intelligence (AI) for a wide range of analytical and practical applications in biomedical areas seems to correlate with the same trend observed in publications in the kidney transplantation field, and points toward the potential of such novel approaches to address the aforementioned aim of improving long-term outcomes of kidney transplant recipients (KTR). However, at the same time, this trend underscores now more than ever the old methodological challenges and potential threats that the research and clinical community needs to be aware of and actively look after with regard to AI-driven evidence. The purpose of this narrative mini-review is to explore challenges for obtaining applicable and adequate kidney transplant data for analyses using AI techniques to develop prediction models, and to propose next steps in the field. We make a call to act toward establishing the strong collaborations needed to bring innovative synergies further augmented by AI, which have the potential to impact the long-term care of KTR. We encourage researchers and clinicians to submit their invaluable research, including original clinical and imaging studies, database studies from registries, meta-analyses, and AI research in the kidney transplantation field.
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Chen C, Yang D, Gao S, Zhang Y, Chen L, Wang B, Mo Z, Yang Y, Hei Z, Zhou S. Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation. Respir Res 2021; 22:94. [PMID: 33789673 PMCID: PMC8011203 DOI: 10.1186/s12931-021-01690-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 03/22/2021] [Indexed: 02/06/2023] Open
Abstract
Background Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML) methods. Methods Data of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ML models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model. Results 591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality (P < 0.05). Among the six ML models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%; specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na+, TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time. Conclusion Our study firstly demonstrated that the XGBoost model with 14 common variables might predict postoperative pneumonia in OLT patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12931-021-01690-3.
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Affiliation(s)
- Chaojin Chen
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China
| | - Dong Yang
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, People's Republic of China
| | - Shilong Gao
- Department of Information, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Yihan Zhang
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China
| | - Liubing Chen
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China
| | - Bohan Wang
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, People's Republic of China
| | - Zihan Mo
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, People's Republic of China
| | - Yang Yang
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China.
| | - Ziqing Hei
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China.
| | - Shaoli Zhou
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China.
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Guo K, Fu X, Zhang H, Wang M, Hong S, Ma S. Predicting the postoperative blood coagulation state of children with congenital heart disease by machine learning based on real-world data. Transl Pediatr 2021; 10:33-43. [PMID: 33633935 PMCID: PMC7882284 DOI: 10.21037/tp-20-238] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Postoperative blood coagulation assessment of children with congenital heart disease (CHD) has been developed using a conventional statistical approach. In this study, the machine learning (ML) was used to predict postoperative blood coagulation function of children with CHD, and assess an array of ML models. METHODS This was a retrospective and data mining study. Based on the samples of 1,690 children with CHD, and screening data based on demographic characteristics, conventional coagulation tests (CCTs) and complete blood count (CBC), with a precise data selection process, and the support of data mining and ML algorithms including Decision tree, Naive Bayes, Support Vector Machine (SVM), Adaptive Boost (AdaBoost) and Random Forest model, and explored the best prediction models of postoperative blood coagulation function for children with CHD by models performance measured in the area under the receiver operating characteristic (ROC) curve (AUC), calibration or Lift curves, and further verified the reliability of the models with statistical tests. RESULTS In primary objective prediction, as decision tree, Naive Bayes, SVM, the AUC of our prediction algorithm was 0.81, 0.82, 0.82, respectively. The accuracy rate of the overall forecast has reached more than 75%. Subsequently, we furtherly build improved models. Among them, the true positive rate of the AdaBoost, Random Forest and SVM prediction models reached more than 80% in the ROC curve. These overall accuracy rate indicated a good classification model. Combined calibration curves and Lift curves, the better fit is the SVM model, which predicted postoperative abnormal coagulation, Lift =2.2, postoperative normal coagulation, Lift =1.8. The statistical results furtherly proved the reliability of ML models. The age, sex, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), white blood cell count (WBC) and platelet count (PLT) were the key features for predicting the postoperative blood coagulation state of children with CHD. CONCLUSIONS ML technology and data mining algorithms may be used for outcome prediction in children with CHD for postoperative blood coagulation state based on the bulk of clinical data, especially CBC indictors from the real world.
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Affiliation(s)
- Kai Guo
- Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Xiaoyan Fu
- Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Huimin Zhang
- Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Mengjian Wang
- Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Songlin Hong
- Fane Data Technology Corporation, Tianjin, China
| | - Shuxuan Ma
- Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
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15
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Qiu Y, Su Y, Tu GW, Ju MJ, He HY, Gu ZY, Yang C, Luo Z. Neutrophil-to-Lymphocyte Ratio Predicts Mortality in Adult Renal Transplant Recipients with Severe Community-Acquired Pneumonia. Pathogens 2020; 9:pathogens9110913. [PMID: 33158161 PMCID: PMC7694174 DOI: 10.3390/pathogens9110913] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 10/29/2020] [Accepted: 11/03/2020] [Indexed: 02/07/2023] Open
Abstract
Mortality of renal transplant recipients with severe community-acquired pneumonia (CAP) remains high, despite advances in critical care management. There is still a lack of biomarkers for predicting prognosis of these patients. The present study aimed to investigate the association between neutrophil-to-lymphocyte ratio (NLR) and mortality in renal transplant recipients with severe CAP. A total of 111 renal transplant recipients with severe CAP admitted to the intensive care unit (ICU) were screened for eligibility between 1 January 2009 and 30 November 2018. Patient characteristics and laboratory test results at ICU admission were retrospectively collected. There were 18 non-survivors (22.2%) among 81 patients with severe CAP who were finally included. Non-survivors had a higher NLR level than survivors (26.8 vs. 12.3, p < 0.001). NLR had the greatest power to predict mortality as suggested by area under the curve (0.88 ± 0.04; p < 0.0001) compared to platelet-to-lymphocyte ratio (0.75 ± 0.06; p < 0.01), pneumonia severity index (0.65 ± 0.08; p = 0.05), CURB-65 (0.65 ± 0.08; p = 0.05), and neutrophil count (0.68 ± 0.07; p < 0.01). Multivariate logistic regression models revealed that NLR was associated with hospital and ICU mortality in renal transplant recipients with severe CAP. NLR levels were independently associated with mortality and may be a useful biomarker for predicting poor outcome in renal transplant recipients with severe CAP.
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Affiliation(s)
- Yue Qiu
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Q.); (Y.S.); (G.-W.T.); (M.-J.J.); (H.-Y.H.); (Z.-Y.G.)
| | - Ying Su
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Q.); (Y.S.); (G.-W.T.); (M.-J.J.); (H.-Y.H.); (Z.-Y.G.)
| | - Guo-Wei Tu
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Q.); (Y.S.); (G.-W.T.); (M.-J.J.); (H.-Y.H.); (Z.-Y.G.)
| | - Min-Jie Ju
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Q.); (Y.S.); (G.-W.T.); (M.-J.J.); (H.-Y.H.); (Z.-Y.G.)
| | - Hong-Yu He
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Q.); (Y.S.); (G.-W.T.); (M.-J.J.); (H.-Y.H.); (Z.-Y.G.)
| | - Zhun-Yong Gu
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Q.); (Y.S.); (G.-W.T.); (M.-J.J.); (H.-Y.H.); (Z.-Y.G.)
| | - Cheng Yang
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Fudan Zhangjiang Institute, Shanghai 201203, China
- Correspondence: (C.Y.); (Z.L.)
| | - Zhe Luo
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Q.); (Y.S.); (G.-W.T.); (M.-J.J.); (H.-Y.H.); (Z.-Y.G.)
- Department of Critical Care Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen 361015, China
- Correspondence: (C.Y.); (Z.L.)
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Thongprayoon C, Kaewput W, Kovvuru K, Hansrivijit P, Kanduri SR, Bathini T, Chewcharat A, Leeaphorn N, Gonzalez-Suarez ML, Cheungpasitporn W. Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation. J Clin Med 2020; 9:1107. [PMID: 32294906 PMCID: PMC7230205 DOI: 10.3390/jcm9041107] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 04/09/2020] [Indexed: 02/07/2023] Open
Abstract
Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as "big data", has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Karthik Kovvuru
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Swetha R. Kanduri
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85721, USA;
| | - Api Chewcharat
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Napat Leeaphorn
- Department of Nephrology, Department of Medicine, Saint Luke’s Health System, Kansas City, MO 64111, USA;
| | - Maria L. Gonzalez-Suarez
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
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