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Assis de Souza A, Stubbs AP, Hesselink DA, Baan CC, Boer K. Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation. Transplantation 2024:00007890-990000000-00768. [PMID: 38773859 DOI: 10.1097/tp.0000000000005063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2024]
Abstract
Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learning (ML) to answer diagnostic, prognostic, and therapeutic questions for many years. Nevertheless, despite the question of whether AI models add value to traditional modeling approaches, such as regression models, their "black box" nature is one of the factors that have hindered the translation from research to clinical practice. Several techniques that make such models understandable to humans were developed with the promise of increasing transparency in the support of medical decision-making. These techniques should help AI to close the gap between theory and practice by yielding trust in the model by doctors and patients, allowing model auditing, and facilitating compliance with emergent AI regulations. But is this also happening in the field of kidney transplantation? This review reports the use and explanation of "black box" models to diagnose and predict kidney allograft rejection, delayed graft function, graft failure, and other related outcomes after kidney transplantation. In particular, we emphasize the discussion on the need (or not) to explain ML models for biological discovery and clinical implementation in kidney transplantation. We also discuss promising future research paths for these computational tools.
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Affiliation(s)
- Alvaro Assis de Souza
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Andrew P Stubbs
- Department of Pathology and Clinical Bioinformatics, Erasmus MC Stubbs Group, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Dennis A Hesselink
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Carla C Baan
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Karin Boer
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
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Sikosana ML, Reeve J, Madill-Thomsen KS, Halloran PF. Using Regression Equations to Enhance Interpretation of Histology Lesions of Kidney Transplant Rejection. Transplantation 2024; 108:445-454. [PMID: 37726883 PMCID: PMC10798587 DOI: 10.1097/tp.0000000000004783] [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: 05/05/2023] [Revised: 06/13/2023] [Accepted: 07/07/2023] [Indexed: 09/21/2023]
Abstract
BACKGROUND The Banff system for histologic diagnosis of rejection in kidney transplant biopsies uses guidelines to assess designated features-lesions, donor-specific antibody (DSA), and C4d staining. We explored whether using regression equations to interpret the features as well as current guidelines could establish the relative importance of each feature and improve histologic interpretation. METHODS We developed logistic regression equations using the designated features to predict antibody-mediated rejection (AMR/mixed) and T-cell-mediated rejection (TCMR/mixed) in 1679 indication biopsies from the INTERCOMEX study ( ClinicalTrials.gov NCT01299168). Equations were trained on molecular diagnoses independent of the designated features. RESULTS In regression and random forests, the important features predicting molecular rejection were as follows: for AMR, ptc and g, followed by cg; for TCMR, t > i. V-lesions were relatively unimportant. C4d and DSA were also relatively unimportant for predicting AMR: by AUC, the model excluding them (0.853) was nearly as good as the model including them (0.860). Including time posttransplant slightly but significantly improved all models. By AUC, regression predicted molecular AMR and TCMR better than Banff histologic diagnoses. More importantly, in biopsies called "no rejection" by Banff guidelines, regression equations based on histology features identified histologic and molecular rejection-related changes in some biopsies and improved survival predictions. Thus, regression can screen for missed rejection. CONCLUSIONS Using lesion-based regression equations in addition to Banff histology guidelines defines the relative important of histology features for identifying rejection, allows screening for potential missed diagnoses, and permits early estimates of AMR when C4d and DSA are not available.
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Affiliation(s)
- Majid L.N. Sikosana
- Division of Nephrology and Transplant Immunology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Jeff Reeve
- Alberta Transplant Applied Genomics Centre, Edmonton, Alberta, Canada
| | | | - Philip F. Halloran
- Division of Nephrology and Transplant Immunology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
- Alberta Transplant Applied Genomics Centre, Edmonton, Alberta, Canada
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Naderi Yaghouti AR, Zamanian H, Shalbaf A. Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters. Sci Rep 2024; 14:2442. [PMID: 38287043 PMCID: PMC10824722 DOI: 10.1038/s41598-024-51741-0] [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: 11/05/2023] [Accepted: 01/09/2024] [Indexed: 01/31/2024] Open
Abstract
This study aims to develop a machine learning approach leveraging clinical data and blood parameters to predict non-alcoholic steatohepatitis (NASH) based on the NAFLD Activity Score (NAS). Using a dataset of 181 patients, we performed preprocessing including normalization and categorical encoding. To identify predictive features, we applied sequential forward selection (SFS), chi-square, analysis of variance (ANOVA), and mutual information (MI). The selected features were used to train machine learning classifiers including SVM, random forest, AdaBoost, LightGBM, and XGBoost. Hyperparameter tuning was done for each classifier using randomized search. Model evaluation was performed using leave-one-out cross-validation over 100 repetitions. Among the classifiers, random forest, combined with SFS feature selection and 10 features, obtained the best performance: Accuracy: 81.32% ± 6.43%, Sensitivity: 86.04% ± 6.21%, Specificity: 70.49% ± 8.12% Precision: 81.59% ± 6.23%, and F1-score: 83.75% ± 6.23% percent. Our findings highlight the promise of machine learning in enhancing early diagnosis of NASH and provide a compelling alternative to conventional diagnostic techniques. Consequently, this study highlights the promise of machine learning techniques in enhancing early and non-invasive diagnosis of NASH based on readily available clinical and blood data. Our findings provide the basis for developing scalable approaches that can improve screening and monitoring of NASH progression.
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Affiliation(s)
- Amir Reza Naderi Yaghouti
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Hamed Zamanian
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Badrouchi S, Bacha MM, Ahmed A, Ben Abdallah T, Abderrahim E. Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence. Sci Rep 2023; 13:21273. [PMID: 38042904 PMCID: PMC10693633 DOI: 10.1038/s41598-023-48645-w] [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: 01/16/2023] [Accepted: 11/29/2023] [Indexed: 12/04/2023] Open
Abstract
The ability to accurately predict long-term kidney transplant survival can assist nephrologists in making therapeutic decisions. However, predicting kidney transplantation (KT) outcomes is challenging due to the complexity of the factors involved. Artificial intelligence (AI) has become an increasingly important tool in the prediction of medical outcomes. Our goal was to utilize both conventional and AI-based methods to predict long-term kidney transplant survival. Our study included 407 KTs divided into two groups (group A: with a graft lifespan greater than 5 years and group B: with poor graft survival). We first performed a traditional statistical analysis and then developed predictive models using machine learning (ML) techniques. Donors in group A were significantly younger. The use of Mycophenolate Mofetil (MMF) was the only immunosuppressive drug that was significantly associated with improved graft survival. The average estimated glomerular filtration rate (eGFR) in the 3rd month post-KT was significantly higher in group A. The number of hospital readmissions during the 1st year post-KT was a predictor of graft survival. In terms of early post-transplant complications, delayed graft function (DGF), acute kidney injury (AKI), and acute rejection (AR) were significantly associated with poor graft survival. Among the 35 AI models developed, the best model had an AUC of 89.7% (Se: 91.9%; Sp: 87.5%). It was based on ten variables selected by an ML algorithm, with the most important being hypertension and a history of red-blood-cell transfusion. The use of AI provided us with a robust model enabling fast and precise prediction of 5-year graft survival using early and easily collectible variables. Our model can be used as a decision-support tool to early detect graft status.
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Affiliation(s)
- Samarra Badrouchi
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia.
- Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia.
- Laboratory of Kidney Transplantation Immunology and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis, Tunisia.
| | - Mohamed Mongi Bacha
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia
- Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
- Laboratory of Kidney Transplantation Immunology and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis, Tunisia
| | - Abdulaziz Ahmed
- Department of Health Services Administration, School of Health Professions, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Taieb Ben Abdallah
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia
- Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
- Laboratory of Kidney Transplantation Immunology and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis, Tunisia
| | - Ezzedine Abderrahim
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia
- Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
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Li JW, Lin D, Che Y, Lv JJ, Chen RJ, Wang LJ, Zeng XX, Ren JC, Zhao HM, Lu X. An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method. Front Neurosci 2023; 17:1221512. [PMID: 37547144 PMCID: PMC10397731 DOI: 10.3389/fnins.2023.1221512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 06/30/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Efficiently recognizing emotions is a critical pursuit in brain-computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition. Methods These features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups. Results The best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83-92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects. Discussion Compared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition.
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Affiliation(s)
- Jia Wen Li
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
- Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan, China
- Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, China
| | - Di Lin
- Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, China
- New Engineering Industry College, Putian University, Putian, China
| | - Yan Che
- Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, China
- New Engineering Industry College, Putian University, Putian, China
| | - Ju Jian Lv
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Rong Jun Chen
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Lei Jun Wang
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Xian Xian Zeng
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Jin Chang Ren
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
- National Subsea Centre, Robert Gordon University, Aberdeen, United Kingdom
| | - Hui Min Zhao
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Xu Lu
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
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Quinino RM, Agena F, Modelli de Andrade LG, Furtado M, Chiavegatto Filho ADP, David-Neto E. A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation. Transplantation 2023; 107:1380-1389. [PMID: 36872507 DOI: 10.1097/tp.0000000000004510] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
BACKGROUND After kidney transplantation (KTx), the graft can evolve from excellent immediate graft function (IGF) to total absence of function requiring dialysis. Recipients with IGF do not seem to benefit from using machine perfusion, an expensive procedure, in the long term when compared with cold storage. This study proposes to develop a prediction model for IGF in KTx deceased donor patients using machine learning algorithms. METHODS Unsensitized recipients who received their first KTx deceased donor between January 1, 2010, and December 31, 2019, were classified according to the conduct of renal function after transplantation. Variables related to the donor, recipient, kidney preservation, and immunology were used. The patients were randomly divided into 2 groups: 70% were assigned to the training and 30% to the test group. Popular machine learning algorithms were used: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Gradient Boosting classifier, Logistic Regression, CatBoost classifier, AdaBoost classifier, and Random Forest classifier. Comparative performance analysis on the test dataset was performed using the results of the AUC values, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. RESULTS Of the 859 patients, 21.7% (n = 186) had IGF. The best predictive performance resulted from the eXtreme Gradient Boosting model (AUC, 0.78; 95% CI, 0.71-0.84; sensitivity, 0.64; specificity, 0.78). Five variables with the highest predictive value were identified. CONCLUSIONS Our results indicated the possibility of creating a model for the prediction of IGF, enhancing the selection of patients who would benefit from an expensive treatment, as in the case of machine perfusion preservation.
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Affiliation(s)
- Raquel M Quinino
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Fabiana Agena
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | | | - Mariane Furtado
- Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brazil
| | | | - Elias David-Neto
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
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Prediction of Prednisolone Dose Correction Using Machine Learning. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:84-103. [PMID: 36910914 PMCID: PMC9995628 DOI: 10.1007/s41666-023-00128-3] [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: 06/21/2022] [Revised: 11/20/2022] [Accepted: 02/03/2023] [Indexed: 02/17/2023]
Abstract
Wrong dose, a common prescription error, can cause serious patient harm, especially in the case of high-risk drugs like oral corticosteroids. This study aims to build a machine learning model to predict dose-related prescription modifications for oral prednisolone tablets (i.e., highly imbalanced data with very few positive cases). Prescription data were obtained from the electronic medical records at a single institute. Cluster analysis classified the clinical departments into six clusters with similar patterns of prednisolone prescription. Two patterns of training datasets were created with/without preprocessing by the SMOTE method. Five ML models (SVM, KNN, GB, RF, and BRF) and logistic regression (LR) models were constructed by Python. The model was internally validated by five-fold stratified cross-validation and was validated with a 30% holdout test dataset. Eighty-two thousand five hundred fifty-three prescribing data for prednisolone tablets containing 135 dose-corrected positive cases were obtained. In the original dataset (without SMOTE), only the BRF model showed a good performance (in test dataset, ROC-AUC:0.917, recall: 0.951). In the training dataset preprocessed by SMOTE, performance was improved on all models. The highest performance models with SMOTE were SVM (in test dataset, ROC-AUC: 0.820, recall: 0.659) and BRF (ROC-AUC: 0.814, recall: 0.634). Although the prescribing data for dose-related collection are highly imbalanced, various techniques such as the following have allowed us to build high-performance prediction models: data preprocessing by SMOTE, stratified cross-validation, and BRF classifier corresponding to imbalanced data. ML is useful in complicated dose audits such as oral prednisolone. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00128-3.
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Aslani N, Galehdar N, Garavand A. A systematic review of data mining applications in kidney transplantation. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
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Badrouchi S, Bacha MM, Hedri H, Ben Abdallah T, Abderrahim E. Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation. J Nephrol 2022; 36:1087-1100. [PMID: 36547773 PMCID: PMC9773693 DOI: 10.1007/s40620-022-01529-0] [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: 05/28/2022] [Accepted: 11/20/2022] [Indexed: 12/24/2022]
Abstract
With its robust ability to integrate and learn from large sets of clinical data, artificial intelligence (AI) can now play a role in diagnosis, clinical decision making, and personalized medicine. It is probably the natural progression of traditional statistical techniques. Currently, there are many unmet needs in nephrology and, more particularly, in the kidney transplantation (KT) field. The complexity and increase in the amount of data, and the multitude of nephrology registries worldwide have enabled the explosive use of AI within the field. Nephrologists in many countries are already at the center of experiments and advances in this cutting-edge technology and our aim is to generalize the use of AI among nephrologists worldwide. In this paper, we provide an overview of AI from a medical perspective. We cover the core concepts of AI relevant to the practicing nephrologist in a consistent and simple way to help them get started, and we discuss the technical challenges. Finally, we focus on the KT field: the unmet needs and the potential role that AI can play to fill these gaps, then we summarize the published KT-related studies, including predictive factors used in each study, which will allow researchers to quickly focus on the most relevant issues.
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Affiliation(s)
- Samarra Badrouchi
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia ,Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Mohamed Mongi Bacha
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia ,Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia ,Laboratory of Kidney Transplantation Immunology and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis, Tunisia
| | - Hafedh Hedri
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia ,Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Taieb Ben Abdallah
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia ,Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia ,Laboratory of Kidney Transplantation Immunology and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis, Tunisia
| | - Ezzedine Abderrahim
- Department of Internal Medicine A, Charles Nicolle Hospital, Tunis, Tunisia ,Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, Tunisia
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Eysenbach G, Pan Y, Zhao L, Niu Z, Guo Q, Zhao B. A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study. J Med Internet Res 2022; 24:e41819. [PMID: 36485032 PMCID: PMC9789495 DOI: 10.2196/41819] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The treatment and care of adults and children with traumatic brain injury (TBI) constitute an intractable global health problem. Predicting the prognosis and length of hospital stay of patients with TBI may improve therapeutic effects and significantly reduce societal health care burden. Applying novel machine learning methods to the field of TBI may be valuable for determining the prognosis and cost-effectiveness of clinical treatment. OBJECTIVE We aimed to combine multiple machine learning approaches to build hybrid models for predicting the prognosis and length of hospital stay for adults and children with TBI. METHODS We collected relevant clinical information from patients treated at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University between May 2017 and May 2022, of which 80% was used for training the model and 20% for testing via screening and data splitting. We trained and tested the machine learning models using 5 cross-validations to avoid overfitting. In the machine learning models, 11 types of independent variables were used as input variables and Glasgow Outcome Scale score, used to evaluate patients' prognosis, and patient length of stay were used as output variables. Once the models were trained, we obtained and compared the errors of each machine learning model from 5 rounds of cross-validation to select the best predictive model. The model was then externally tested using clinical data of patients treated at the First Affiliated Hospital of Anhui Medical University from June 2021 to February 2022. RESULTS The final convolutional neural network-support vector machine (CNN-SVM) model predicted Glasgow Outcome Scale score with an accuracy of 93% and 93.69% in the test and external validation sets, respectively, and an area under the curve of 94.68% and 94.32% in the test and external validation sets, respectively. The mean absolute percentage error of the final built convolutional neural network-support vector regression (CNN-SVR) model predicting inpatient time in the test set and external validation set was 10.72% and 10.44%, respectively. The coefficient of determination (R2) was 0.93 and 0.92 in the test set and external validation set, respectively. Compared with back-propagation neural network, CNN, and SVM models built separately, our hybrid model was identified to be optimal and had high confidence. CONCLUSIONS This study demonstrates the clinical utility of 2 hybrid models built by combining multiple machine learning approaches to accurately predict the prognosis and length of stay in hospital for adults and children with TBI. Application of these models may reduce the burden on physicians when assessing TBI and assist clinicians in the medical decision-making process.
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Affiliation(s)
| | - Yifeng Pan
- The School of Big Data and Artificial Intelligence, Anhui Xinhua University, Hefei, China
| | - Luotong Zhao
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Zhaoyi Niu
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Qingguo Guo
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Bing Zhao
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
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Siddiqui NS, Klein A, Godara A, Buchsbaum RJ, Hughes MC. Predicting In-Hospital Mortality After Acute Myeloid Leukemia Therapy: Through Supervised Machine Learning Algorithms. JCO Clin Cancer Inform 2022; 6:e2200044. [PMID: 36542824 DOI: 10.1200/cci.22.00044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Despite careful patient selection, induction chemotherapy for acute myeloid leukemia (AML) is associated with a considerable risk for treatment-related mortality (5%-20%). We evaluated machine learning (ML) algorithms trained using factors available at the time of admission for AML therapy to predict death during the hospitalization. METHODS We included AML discharges with age > 17 years who received inpatient chemotherapy from State Inpatient Database from Arizona, Florida, New York, Maryland, Washington, and New Jersey for years 2008-2014. The primary objective was to predict inpatient mortality in patients undergoing chemotherapy using covariates present before initiation of chemotherapy. ML algorithms logistic regression (LR), decision tree, and random forest were compared. RESULTS 29,613 hospitalizations for patients with AML were included in the analysis each with 4,177 features. The median age was 58.9 (18-101) years, 13,689 (53.7%) were male, and 20,203 (69%) were White. The mean time from admission to chemotherapy was 3 days (95% CI, 2.9 to 3.1), and 2,682 (9.1%) died during the hospitalization. Both LR and random forest models achieved an area under the curve (AUC) score of 0.78, whereas decision tree achieved an AUC of 0.70. The baseline LR model with age yielded an AUC of 0.62. To clinically balance and minimize false positives, we selected a decision threshold of 0.7 and at this threshold, 51 of our test set of 5,923 could have potentially averted treatment-related mortality. CONCLUSION Using readily accessible variables, inpatient mortality of patients on track for chemotherapy to treat AML can be predicted through ML algorithms. The model also predicted inpatient mortality when tested on different data representations and paves the way for future research.
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Affiliation(s)
- Nauman S Siddiqui
- Division of Hematology, Medical Oncology and Palliative Care, School of Medicine and Public Health, University of Wisconsin, Madison, WI
| | | | - Amandeep Godara
- Division of Hematology and Hematologic Malignancies, University of Utah, Salt Lake City, UT
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Jin S, Zhang X, Liu H, Hao J, Cao K, Lin C, Yusufu M, Hu N, Hu A, Wang N. Identification of the Optimal Model for the Prediction of Diabetic Retinopathy in Chinese Rural Population: Handan Eye Study. J Diabetes Res 2022; 2022:4282953. [PMID: 36440469 PMCID: PMC9683953 DOI: 10.1155/2022/4282953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/01/2022] [Accepted: 11/05/2022] [Indexed: 11/17/2022] Open
Abstract
Background To identify an optimal model for diabetic retinopathy (DR) prediction in Chinese rural population by establishing and comparing different algorithms based on the data from Handan Eye Study (HES). Methods Five algorithms, including multivariable logistic regression (MLR), classification and regression trees (C&RT), support vector machine (SVM), random forests (RF), and gradient boosting machine (GBM), were used to establish DR prediction models with HES data. The performance of the models was assessed based on the adjusted area under the ROC curve (AUROC), sensitivity, specificity, and accuracy. Results The data on 4752 subjects were used to build the DR prediction model, and among them, 198 patients were diagnosed with DR. The age of the included subjects ranged from 30 to 85 years old, with an average age of 50.9 years (SD = 3.04). The kappa coefficient of the diagnosis between the two ophthalmologists was 0.857. The MLR model revealed that blood glucose, systolic blood pressure, and body mass index were independently associated with the development of DR. The AUROC obtained by GBM (0.952), RF (0.949), and MLR (0.936) was similar and statistically larger than that of CART (0.682) and SVM (0.765). Conclusions The MLR model exhibited excellent prediction performance and visible equation and thus was the optimal model for DR prediction. Therefore, the MLR model may have the potential to serve as a complementary screening tool for the early detection of DR, especially in remote and underserved areas.
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Affiliation(s)
- Shanshan Jin
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital of Capital Medical University, Hougou Lane No 17, Chongnei Street, Beijing 100005, China
| | - Xu Zhang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital of Capital Medical University, Hougou Lane No 17, Chongnei Street, Beijing 100005, China
| | - Hanruo Liu
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital of Capital Medical University, Hougou Lane No 17, Chongnei Street, Beijing 100005, China
| | - Jie Hao
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital of Capital Medical University, Hougou Lane No 17, Chongnei Street, Beijing 100005, China
| | - Kai Cao
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital of Capital Medical University, Hougou Lane No 17, Chongnei Street, Beijing 100005, China
| | - Caixia Lin
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital of Capital Medical University, Hougou Lane No 17, Chongnei Street, Beijing 100005, China
| | - Mayinuer Yusufu
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital of Capital Medical University, Hougou Lane No 17, Chongnei Street, Beijing 100005, China
| | - Na Hu
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital of Capital Medical University, Hougou Lane No 17, Chongnei Street, Beijing 100005, China
| | - Ailian Hu
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital of Capital Medical University, Hougou Lane No 17, Chongnei Street, Beijing 100005, China
| | - Ningli Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital of Capital Medical University, Hougou Lane No 17, Chongnei Street, Beijing 100005, China
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13
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Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1249692. [PMID: 35509861 PMCID: PMC9060999 DOI: 10.1155/2022/1249692] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/29/2022] [Indexed: 11/23/2022]
Abstract
Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.
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Pontrelli P, Simone S, Rascio F, Pesce F, Conserva F, Infante B, Castellano G, Sallustio F, Fiorentino M, Zaza G, Gallone A, Battaglia M, Ditonno P, Stallone G, Gesualdo L, Grandaliano G. Pre-Transplant Expression of CCR-2 in Kidney Transplant Recipients Is Associated With the Development of Delayed Graft Function. Front Immunol 2022; 13:804762. [PMID: 35371047 PMCID: PMC8967482 DOI: 10.3389/fimmu.2022.804762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 02/11/2022] [Indexed: 01/17/2023] Open
Abstract
Background Delayed graft function (DGF) leads to a reduced graft survival. Donors’ features have been always considered as key pathogenic factors in this setting. The aim of our study was to evaluate the recipients’ characteristics in the development of DGF. Methods We enrolled 932 kidney graft recipients from 466 donors; 226 recipients experienced DGF. In 290 donors, both recipients presented with early graft function (EGF, group A), in 50 both recipients experienced DGF (group B), and in 126 one recipient presented with DGF and the other with EGF (group C). In group C, we selected 7 couples of DGF/EGF recipients and we evaluated the transcriptomic profile by microarray on circulating mononuclear cells harvested before transplantation. Results were validated by qPCR in an independent group of 25 EGF/DGF couples. Findings In the whole study group, DGF was associated with clinical characteristics related to both donors and recipient. In group C, DGF was significantly associated with body mass index, hemodialysis, and number of mismatches. In the same group, we identified 411 genes differently expressed before transplantation between recipients discordant for the transplant outcome. Those genes were involved in immune dysfunction and inflammation. In particular, we observed a significant increase in DGF patients in the expression of C–C chemokine receptor type 2 (CCR2), the monocyte chemoattractant protein-1 (MCP-1) receptor. CCR-2 upregulation was confirmed in an independent cohort of patients. Conclusions Our results suggest that recipients’ clinical/immunological features, potentially modulated by dialysis, are associated with the development of DGF independently of donors’ features.
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Affiliation(s)
- Paola Pontrelli
- Department of Emergency and Organ Transplantation, Nephrology and Urology Units, University of Bari Aldo Moro, Bari, Italy
| | - Simona Simone
- Department of Emergency and Organ Transplantation, Nephrology and Urology Units, University of Bari Aldo Moro, Bari, Italy
| | - Federica Rascio
- Department of Medical and Surgical Sciences, Renal Unit, University of Foggia, Foggia, Italy
| | - Francesco Pesce
- Department of Emergency and Organ Transplantation, Nephrology and Urology Units, University of Bari Aldo Moro, Bari, Italy
| | - Francesca Conserva
- Department of Emergency and Organ Transplantation, Nephrology and Urology Units, University of Bari Aldo Moro, Bari, Italy
| | - Barbara Infante
- Department of Medical and Surgical Sciences, Renal Unit, University of Foggia, Foggia, Italy
| | - Giuseppe Castellano
- Department of Clinical Sciences and Community Health, Nephrology Unit, University of Milano and Fondazione Cà Grande Ospedale Maggiore Policlinico, Milano, Italy
| | - Fabio Sallustio
- Department of Internal Medicine, University of Bari Aldo Moro, Bari, Italy
| | - Marco Fiorentino
- Department of Emergency and Organ Transplantation, Nephrology and Urology Units, University of Bari Aldo Moro, Bari, Italy
| | - Gianluigi Zaza
- Department of Medical and Surgical Sciences, Renal Unit, University of Foggia, Foggia, Italy
| | - Anna Gallone
- Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Michele Battaglia
- Department of Emergency and Organ Transplantation, Nephrology and Urology Units, University of Bari Aldo Moro, Bari, Italy
| | - Pasquale Ditonno
- Department of Emergency and Organ Transplantation, Nephrology and Urology Units, University of Bari Aldo Moro, Bari, Italy
| | - Giovanni Stallone
- Department of Medical and Surgical Sciences, Renal Unit, University of Foggia, Foggia, Italy
| | - Loreto Gesualdo
- Department of Emergency and Organ Transplantation, Nephrology and Urology Units, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Grandaliano
- Department of Translational Medicine and Surgery, Nephrology Unit, Università Cattolica del Sacro Cuore and Fondazione Policlinico Universitario "A. Gemelli", Rome, Italy
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15
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Artificial Intelligence-A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant. J Clin Med 2021; 10:jcm10225244. [PMID: 34830526 PMCID: PMC8618905 DOI: 10.3390/jcm10225244] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/02/2021] [Accepted: 11/10/2021] [Indexed: 11/17/2022] Open
Abstract
Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor’s eGFR, recipient’s BMI, donor’s BMI, and recipient–donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor’s age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieved an accuracy of 93.75% and an AUC of 0.92. Using machine learning, we were able to assess the risk of DGF in recipients after kidney transplant from a deceased donor. Our solution is scalable and can be improved during subsequent transplants. Based on the new data, the models can achieve better outcomes.
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16
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Abstract
BACKGROUND Despite the kidney supply shortage, 18%-20% of deceased donor kidneys are discarded annually in the United States. In 2018, 3569 kidneys were discarded. METHODS We compared machine learning (ML) techniques to identify kidneys at risk of discard at the time of match run and after biopsy and machine perfusion results become available. The cohort consisted of adult deceased donor kidneys donated between December 4, 2014, and July 1, 2019. The studied ML models included Random Forests (RF), Adaptive Boosting (AdaBoost), Neural Networks (NNet), Support Vector Machines (SVM), and K-nearest Neighbors (KNN). In addition, a Logistic Regression (LR) model was fitted and used for comparison with the ML models' performance. RESULTS RF outperformed other ML models. Of 8036 discarded kidneys in the test dataset, LR correctly classified 3422 kidneys, whereas RF correctly classified 4762 kidneys (area under the receiver operative curve [AUC]: 0.85 versus 0.888, and balanced accuracy: 0.681 versus 0.759). For the kidneys with kidney donor profile index of >85% (6079 total), RF significantly outperformed LR in classifying discard and transplant prediction (AUC: 0.814 versus 0.717, and balanced accuracy: 0.732 versus 0.657). More than 388 kidneys were correctly classified using RF. Including biopsy and machine perfusion variables improved the performance of LR and RF (LR's AUC: 0.888 and balanced accuracy: 0.74 versus RF's AUC: 0.904 and balanced accuracy: 0.775). CONCLUSIONS Kidneys that are at risk of discard can be more accurately identified using ML techniques such as RF.
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Affiliation(s)
- Masoud Barah
- Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL
| | - Sanjay Mehrotra
- Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL
- Center for Engineering and Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Northwestern University Feinberg School of Medicine, Chicago, IL
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17
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Connor KL, O'Sullivan ED, Marson LP, Wigmore SJ, Harrison EM. The Future Role of Machine Learning in Clinical Transplantation. Transplantation 2021; 105:723-735. [PMID: 32826798 DOI: 10.1097/tp.0000000000003424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.
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Affiliation(s)
- Katie L Connor
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Eoin D O'Sullivan
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Lorna P Marson
- Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen J Wigmore
- Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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18
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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: 9] [Impact Index Per Article: 3.0] [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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19
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Elfanagely O, Toyoda Y, Othman S, Mellia JA, Basta M, Liu T, Kording K, Ungar L, Fischer JP. Machine Learning and Surgical Outcomes Prediction: A Systematic Review. J Surg Res 2021; 264:346-361. [PMID: 33848833 DOI: 10.1016/j.jss.2021.02.045] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 02/13/2021] [Accepted: 02/27/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND Machine learning (ML) has garnered increasing attention as a means to quantitatively analyze the growing and complex medical data to improve individualized patient care. We herein aim to critically examine the current state of ML in predicting surgical outcomes, evaluate the quality of currently available research, and propose areas of improvement for future uses of ML in surgery. METHODS A systematic review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) checklist. PubMed, MEDLINE, and Embase databases were reviewed under search syntax "machine learning" and "surgery" for papers published between 2015 and 2020. RESULTS Of the initial 2677 studies, 45 papers met inclusion and exclusion criteria. Fourteen different subspecialties were represented with neurosurgery being most common. The most frequently used ML algorithms were random forest (n = 19), artificial neural network (n = 17), and logistic regression (n = 17). Common outcomes included postoperative mortality, complications, patient reported quality of life and pain improvement. All studies which compared ML algorithms to conventional studies which used area under the curve (AUC) to measure accuracy found improved outcome prediction with ML models. CONCLUSIONS While still in its early stages, ML models offer surgeons an opportunity to capitalize on the myriad of clinical data available and improve individualized patient care. Limitations included heterogeneous outcome and imperfect quality of some of the papers. We therefore urge future research to agree upon methods of outcome reporting and require basic quality standards.
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Affiliation(s)
- Omar Elfanagely
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Yoshiko Toyoda
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sammy Othman
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joseph A Mellia
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Marten Basta
- Department of Plastic and Reconstructive Surgery, Brown University, Providence, Rhode Island
| | - Tony Liu
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Konrad Kording
- Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Lyle Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John P Fischer
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
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20
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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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21
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Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning. Sci Rep 2020; 10:20140. [PMID: 33208887 PMCID: PMC7676251 DOI: 10.1038/s41598-020-76974-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 10/29/2020] [Indexed: 12/19/2022] Open
Abstract
Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diameter, cyst size, mural nodule, and tumour location were factors considered for model development (MD). After the division into a MD set and a test set (2:1), the best ML and LR models were developed by training with the MD set using a tenfold cross validation. The test area under the receiver operating curves (AUCs) of the two models were calculated using an independent test set. A total of 3,708 patients were included. The stacked ensemble algorithm in the ML model and variable combinations containing all variables in the LR model were the most chosen during 200 repetitions. After 200 repetitions, the mean AUCs of the ML and LR models were comparable (0.725 vs. 0.725). The performances of the ML and LR models were comparable. The LR model was more practical than ML counterpart, because of its convenience in clinical use and simple interpretability.
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22
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Bae S, Massie AB, Caffo BS, Jackson KR, Segev DL. Machine learning to predict transplant outcomes: helpful or hype? A national cohort study. Transpl Int 2020; 33:1472-1480. [PMID: 32996170 PMCID: PMC8269970 DOI: 10.1111/tri.13695] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 11/30/2019] [Accepted: 06/29/2020] [Indexed: 12/13/2022]
Abstract
An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a "common" analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased-donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/35 644 recipients). Using the training set, we performed regression and ML procedures [gradient boosting (GB) and random forests (RF)] to predict delayed graft function, one-year acute rejection, death-censored graft failure C, all-cause graft failure, and death. Their performances were compared on the validation set using -statistics. In predicting rejection, regression (C = 0.601 0.6110.621 ) actually outperformed GB (C = 0.581 0.5910.601 ) and RF (C = 0.569 0.5790.589 ). For all other outcomes, the C-statistics were nearly identical across methods (delayed graft function, 0.717-0.723; death-censored graft failure, 0.637-0.642; all-cause graft failure, 0.633-0.635; and death, 0.705-0.708). Given its shortcomings in model interpretability and hypothesis testing, ML is advantageous only when it clearly outperforms conventional regression; in the case of transplant outcomes prediction, ML seems more hype than helpful.
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Affiliation(s)
- Sunjae Bae
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Allan B Massie
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Kyle R Jackson
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Dorry L Segev
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
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23
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Kawakita S, Beaumont JL, Jucaud V, Everly MJ. Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning. Sci Rep 2020; 10:18409. [PMID: 33110142 PMCID: PMC7591492 DOI: 10.1038/s41598-020-75473-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 10/15/2020] [Indexed: 02/06/2023] Open
Abstract
Machine learning (ML) has shown its potential to improve patient care over the last decade. In organ transplantation, delayed graft function (DGF) remains a major concern in deceased donor kidney transplantation (DDKT). To this end, we harnessed ML to build personalized prognostic models to predict DGF. Registry data were obtained on adult DDKT recipients for model development (n = 55,044) and validation (n = 6176). Incidence rates of DGF were 25.1% and 26.3% for the development and validation sets, respectively. Twenty-six predictors were identified via recursive feature elimination with random forest. Five widely-used ML algorithms-logistic regression (LR), elastic net, random forest, artificial neural network (ANN), and extreme gradient boosting (XGB) were trained and compared with a baseline LR model fitted with previously identified risk factors. The new ML models, particularly ANN with the area under the receiver operating characteristic curve (ROC-AUC) of 0.732 and XGB with ROC-AUC of 0.735, exhibited superior performance to the baseline model (ROC-AUC = 0.705). This study demonstrates the use of ML as a viable strategy to enable personalized risk quantification for medical applications. If successfully implemented, our models may aid in both risk quantification for DGF prevention clinical trials and personalized clinical decision making.
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Affiliation(s)
| | | | - Vadim Jucaud
- Terasaki Research Institute, Los Angeles, CA, USA
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24
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Yang L, Liu Q, Zhao Q, Zhu X, Wang L. Machine learning is a valid method for predicting prehospital delay after acute ischemic stroke. Brain Behav 2020; 10:e01794. [PMID: 32812396 PMCID: PMC7559608 DOI: 10.1002/brb3.1794] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 07/15/2020] [Accepted: 07/20/2020] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVES This study aimed to identify the influencing factors associated with long onset-to-door time and establish predictive models that could help to assess the probability of prehospital delay in populations with a high risk for stroke. MATERIALS AND METHODS Patients who were diagnosed with acute ischemic stroke (AIS) and hospitalized between 1 November 2018 and 31 July 2019 were interviewed, and their medical records were extracted for data analysis. Two machine learning algorithms (support vector machine and Bayesian network) were applied in this study, and their predictive performance was compared with that of the classical logistic regression models after using several variable selection methods. Timely admission (onset-to-door time < 3 hr) and prehospital delay (onset-to-door time ≥ 3 hr) were the outcome variables. We computed the area under curve (AUC) and the difference in the mean AUC values between the models. RESULTS A total of 450 patients with AIS were enrolled; 57 (12.7%) with timely admission and 393 (87.3%) patients with prehospital delay. All models, both those constructed by logistic regression and those by machine learning, performed well in predicting prehospital delay (range mean AUC: 0.800-0.846). The difference in the mean AUC values between the best performing machine learning model and the best performing logistic regression model was negligible (0.014; 95% CI: 0.013-0.015). CONCLUSIONS Machine learning algorithms were not inferior to logistic regression models for prediction of prehospital delay after stroke. All models provided good discrimination, thereby creating valuable diagnostic programs for prehospital delay prediction.
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Affiliation(s)
- Li Yang
- School of Nursing, Qingdao University, Qingdao, China
| | - Qinqin Liu
- School of Nursing, The second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Qiuli Zhao
- School of Nursing, The second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Xuemei Zhu
- School of Nursing, The second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Ling Wang
- School of Nursing, The second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
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25
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Burlacu A, Iftene A, Jugrin D, Popa IV, Lupu PM, Vlad C, Covic A. Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review. BIOMED RESEARCH INTERNATIONAL 2020; 2020:9867872. [PMID: 32596403 PMCID: PMC7303737 DOI: 10.1155/2020/9867872] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/15/2020] [Accepted: 05/25/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used? METHODS We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. RESULTS AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues. CONCLUSIONS Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.
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Affiliation(s)
- Alexandru Burlacu
- Department of Interventional Cardiology-Cardiovascular Diseases Institute, Iasi, Romania
- “Grigore T. Popa” University of Medicine, Iasi, Romania
| | - Adrian Iftene
- Faculty of Computer Science, “Alexandru Ioan Cuza” University of Iasi, Romania
| | - Daniel Jugrin
- Center for Studies and Interreligious and Intercultural Dialogue, University of Bucharest, Romania
| | - Iolanda Valentina Popa
- “Grigore T. Popa” University of Medicine, Iasi, Romania
- Institute of Gastroenterology and Hepatology, Iasi, Romania
| | | | - Cristiana Vlad
- “Grigore T. Popa” University of Medicine, Iasi, Romania
- Department of Internal Medicine-Nephrology, Iasi, Romania
| | - Adrian Covic
- “Grigore T. Popa” University of Medicine, Iasi, Romania
- Nephrology Clinic, Dialysis and Renal Transplant Center-‘C.I. Parhon' University Hospital, Iasi, Romania
- The Academy of Romanian Scientists (AOSR), Romania
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Ha CW, Kim SH, Lee DH, Kim H, Park YB. Predictive validity of radiographic signs of complete discoid lateral meniscus in children using machine learning techniques. J Orthop Res 2020; 38:1279-1288. [PMID: 31883134 DOI: 10.1002/jor.24578] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Accepted: 12/04/2019] [Indexed: 02/04/2023]
Abstract
The diagnostic utility of radiographic signs of complete discoid lateral meniscus remains controversial. This study aimed to investigate the diagnostic accuracy and determine which sign is most reliably detects the presence of a complete discoid lateral meniscus in children. A total of 141 knees (age 7-16) with complete discoid lateral meniscus and 141 age- and sex-matched knees with normal meniscus were included. The following radiographic signs were evaluated: lateral joint (LJ) space, fibular head (FH) height, lateral tibial spine (LTS) height, lateral tibial plateau (LTP) obliquity, lateral femoral condyle (LFC) squaring, LTP cupping, LFC notching, and prominence ratio of the femoral condyle. Prediction models were constructed using logistic regressions, decision trees, and random forest analyses. Receiver operating characteristic curves and area under the curve (AUC) were estimated to compare the diagnostic accuracy of the radiographic signs and model fit. The random forest model yielded the best diagnostic accuracy (AUC: 0.909), with 86.5% sensitivity and 82.2% specificity. LJ space height, FH height, and prominence ratio showed statistically large AUC compared with LTS height and LTP obliquity (P < .05 in all). The cut-off values for diagnosing discoid meniscus to be <12.55 mm for FH height, <0.804 for prominence ratio, and >6.6 mm for LJ space height when using the random forest model. On the basis of the results of this study, in clinical practice, LJ space height, FH height and prominence ratio could be easily used as supplementary tools for complete discoid lateral meniscus in children.
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Affiliation(s)
- Chul-Won Ha
- Department of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Seong Hwan Kim
- Department of Orthopedic Surgery, Hyundae General Hospital, Chung-Ang University College of Medicine, Namyangju-si, Gyeonggi-do, South Korea
| | - Dong-Hoon Lee
- Department of Orthopedic Surgery, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Hyojoon Kim
- Department of Computer Science, Princeton University, Princeton, New Jersey
| | - Yong-Beom Park
- Department of Orthopedic Surgery, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea
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Lin GM, Lu HHS. A 12-Lead ECG-Based System With Physiological Parameters and Machine Learning to Identify Right Ventricular Hypertrophy in Young Adults. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2020; 8:1900510. [PMID: 32509473 PMCID: PMC7269457 DOI: 10.1109/jtehm.2020.2996370] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/07/2020] [Accepted: 05/14/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVE The presence of right ventricular hypertrophy (RVH) accounts for approximately 5-10% in young adults. The sensitivity estimated by commonly used 12-lead electrocardiographic (ECG) criteria for identifying the presence of RVH is under 20% in the general population. The aim of this study is to develop a 12-lead ECG system with the related information of age, body height and body weight via machine learning to increase the sensitivity and the precision for detecting RVH. METHOD In a sample of 1,701 males, aged 17-45 years, support vector machine is used for the training of 31 parameters including age, body height and body weight in addition to 28 ECG data such as axes, intervals and wave voltages as the inputs to link the output RVH. The RVH is defined on the echocardiographic finding for young males as right ventricular anterior wall thickness > 5.5 mm. RESULTS On the system goal for increasing sensitivity, the specificity is controlled around 70-75% and all data tested in the proposed method show competent sensitivity up to 70.3%. The values of area under curve of receiver operating characteristic curve and precision-recall curve using the proposed method are 0.780 and 0.285, respectively, which are better than 0.518 and 0.112 using the Sokolow-Lyon voltage criterion, respectively, for detecting unspecific RVH. CONCLUSION We present a method using simple physiological parameters with ECG data to effectively identify more than 70% of the RVH among young adults. Clinical Impact: This system provides a fast, precise and feasible diagnosis tool to screen RVH.
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Affiliation(s)
- Gen-Min Lin
- Department of Preventive MedicineFeinberg School of MedicineNorthwestern UniversityChicagoIL60611USA.,Department of MedicineHualien Armed Forces General HospitalHualien97144Taiwan.,Department of MedicineTri-Service General Hospital, National Defense Medical CenterTaipei11490Taiwan
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Noh J, Yoo KD, Bae W, Lee JS, Kim K, Cho JH, Lee H, Kim DK, Lim CS, Kang SW, Kim YL, Kim YS, Kim G, Lee JP. Prediction of the Mortality Risk in Peritoneal Dialysis Patients using Machine Learning Models: A Nation-wide Prospective Cohort in Korea. Sci Rep 2020; 10:7470. [PMID: 32366838 PMCID: PMC7198502 DOI: 10.1038/s41598-020-64184-0] [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: 11/03/2019] [Accepted: 04/07/2020] [Indexed: 02/06/2023] Open
Abstract
Herein, we aim to assess mortality risk prediction in peritoneal dialysis patients using machine-learning algorithms for proper prognosis prediction. A total of 1,730 peritoneal dialysis patients in the CRC for ESRD prospective cohort from 2008 to 2014 were enrolled in this study. Classification algorithms were used for prediction of N-year mortality including neural network. The survival hazard ratio was presented by machine-learning algorithms using survival statistics and was compared to conventional algorithms. A survival-tree algorithm presented the most accurate prediction model and outperformed a conventional method such as Cox regression (concordance index 0.769 vs 0.745). Among various survival decision-tree models, the modified Charlson Comorbidity index (mCCI) was selected as the best predictor of mortality. If peritoneal dialysis patients with high mCCI (>4) were aged ≥70.5 years old, the survival hazard ratio was predicted as 4.61 compared to the overall study population. Among the various algorithm using longitudinal data, the AUC value of logistic regression was augmented at 0.804. In addition, the deep neural network significantly improved performance to 0.841. We propose machine learning-based final model, mCCI and age were interrelated as notable risk factors for mortality in Korean peritoneal dialysis patients.
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Affiliation(s)
- Junhyug Noh
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, South Korea
| | - Kyung Don Yoo
- Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, South Korea
| | - Wonho Bae
- College of Information and Computer Sciences, University of Massachusetts Amherst, Massachusetts, United States
| | - Jong Soo Lee
- Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, South Korea
| | - Kangil Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea
| | - Jang-Hee Cho
- Department of Internal Medicine, Kyungpook National University College of Medicine, Daegu, South Korea
| | - Hajeong Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Dong Ki Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine Seoul National University College of Medicine, Seoul, South Korea
| | - Chun Soo Lim
- Department of Internal Medicine Seoul National University College of Medicine, Seoul, South Korea
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Shin-Wook Kang
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Yong-Lim Kim
- Department of Internal Medicine, Kyungpook National University College of Medicine, Daegu, South Korea
| | - Yon Su Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine Seoul National University College of Medicine, Seoul, South Korea
| | - Gunhee Kim
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, South Korea.
| | - Jung Pyo Lee
- Department of Internal Medicine Seoul National University College of Medicine, Seoul, South Korea.
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, South Korea.
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Gravesteijn BY, Nieboer D, Ercole A, Lingsma HF, Nelson D, van Calster B, Steyerberg EW. Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury. J Clin Epidemiol 2020; 122:95-107. [PMID: 32201256 DOI: 10.1016/j.jclinepi.2020.03.005] [Citation(s) in RCA: 98] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 02/04/2020] [Accepted: 03/09/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVE We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. STUDY DESIGN AND SETTING We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. RESULTS In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. CONCLUSION ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations.
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Affiliation(s)
- Benjamin Y Gravesteijn
- Departments of Public Health, Erasmus MC - University Medical Centre Rotterdam, Postbus 2040, 3000 CA, Rotterdam, the Netherlands.
| | - Daan Nieboer
- Departments of Public Health, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, the Netherlands
| | - Ari Ercole
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Hester F Lingsma
- Departments of Public Health, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, the Netherlands
| | - David Nelson
- Department of Physiology and Pharmacology, Section of Perioperative Medicine and Intensive Care, Karolinska Institutet, Stockholm, Sweden
| | - Ben van Calster
- Department of Development and Regeneration, KU Leuven, Belgium; Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands
| | - Ewout W Steyerberg
- Departments of Public Health, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, the Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands
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Costa SD, de Andrade LGM, Barroso FVC, de Oliveira CMC, Daher EDF, Fernandes PFCBC, Esmeraldo RDM, de Sandes-Freitas TV. The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. PLoS One 2020; 15:e0228597. [PMID: 32027717 PMCID: PMC7004552 DOI: 10.1371/journal.pone.0228597] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 01/18/2020] [Indexed: 12/23/2022] Open
Abstract
Background This study evaluated the risk factors for delayed graft function (DGF) in a country where its incidence is high, detailing donor maintenance-related (DMR) variables and using machine learning (ML) methods beyond the traditional regression-based models. Methods A total of 443 brain dead deceased donor kidney transplants (KT) from two Brazilian centers were retrospectively analyzed and the following DMR were evaluated using predictive modeling: arterial blood gas pH, serum sodium, blood glucose, urine output, mean arterial pressure, vasopressors use, and reversed cardiac arrest. Results Most patients (95.7%) received kidneys from standard criteria donors. The incidence of DGF was 53%. In multivariable logistic regression analysis, DMR variables did not impact on DGF occurrence. In post-hoc analysis including only KT with cold ischemia time<21h (n = 220), urine output in 24h prior to recovery surgery (OR = 0.639, 95%CI 0.444–0.919) and serum sodium (OR = 1.030, 95%CI 1.052–1.379) were risk factors for DGF. Using elastic net regularized regression model and ML analysis (decision tree, neural network and support vector machine), urine output and other DMR variables emerged as DGF predictors: mean arterial pressure, ≥ 1 or high dose vasopressors and blood glucose. Conclusions Some DMR variables were associated with DGF, suggesting a potential impact of variables reflecting poor clinical and hemodynamic status on the incidence of DGF.
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Affiliation(s)
- Silvana Daher Costa
- Department of Clinical Medicine, Faculty of Medicine, Federal University of Ceará, Fortaleza, Ceará, Brazil
- Walter Cantídio University Hospital, Fortaleza, Ceará, Brazil
- Hospital Geral de Fortaleza, Fortaleza, Ceará, Brazil
| | | | | | | | | | | | | | - Tainá Veras de Sandes-Freitas
- Department of Clinical Medicine, Faculty of Medicine, Federal University of Ceará, Fortaleza, Ceará, Brazil
- Hospital Geral de Fortaleza, Fortaleza, Ceará, Brazil
- * E-mail:
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Senanayake S, Barnett A, Graves N, Healy H, Baboolal K, Kularatna S. Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study. F1000Res 2019; 8:1810. [PMID: 32419922 PMCID: PMC7199287 DOI: 10.12688/f1000research.20661.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/24/2019] [Indexed: 03/29/2024] Open
Abstract
Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients. Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature. However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia. Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models. The best predictive model will be selected based on the model's performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.
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Affiliation(s)
- Sameera Senanayake
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Adrian Barnett
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Nicholas Graves
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Helen Healy
- Royal Brisbane Hospital for Women, Brisbane, QLD, 4001, Australia
- School of Medicine, University of Queensland, Brisbane, QLD, 4001, Australia
| | - Keshwar Baboolal
- Royal Brisbane Hospital for Women, Brisbane, QLD, 4001, Australia
- School of Medicine, University of Queensland, Brisbane, QLD, 4001, Australia
| | - Sanjeewa Kularatna
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
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Senanayake S, Barnett A, Graves N, Healy H, Baboolal K, Kularatna S. Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study. F1000Res 2019; 8:1810. [PMID: 32419922 PMCID: PMC7199287 DOI: 10.12688/f1000research.20661.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/04/2020] [Indexed: 02/03/2023] Open
Abstract
Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients. Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature. However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia. Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models (for live donor and deceased donor transplants). The best predictive model will be selected based on the model's performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.
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Affiliation(s)
- Sameera Senanayake
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Adrian Barnett
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Nicholas Graves
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
| | - Helen Healy
- Royal Brisbane Hospital for Women, Brisbane, QLD, 4001, Australia
- School of Medicine, University of Queensland, Brisbane, QLD, 4001, Australia
| | - Keshwar Baboolal
- Royal Brisbane Hospital for Women, Brisbane, QLD, 4001, Australia
- School of Medicine, University of Queensland, Brisbane, QLD, 4001, Australia
| | - Sanjeewa Kularatna
- Australian Center for Health Service Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
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Senanayake S, White N, Graves N, Healy H, Baboolal K, Kularatna S. Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models. Int J Med Inform 2019; 130:103957. [PMID: 31472443 DOI: 10.1016/j.ijmedinf.2019.103957] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 07/15/2019] [Accepted: 08/21/2019] [Indexed: 01/11/2023]
Abstract
INTRODUCTION Machine learning has been increasingly used to develop predictive models to diagnose different disease conditions. The heterogeneity of the kidney transplant population makes predicting graft outcomes extremely challenging. Several kidney graft outcome prediction models have been developed using machine learning, and are available in the literature. However, a systematic review of machine learning based prediction methods applied to kidney transplant has not been done to date. The main aim of our study was to perform an in-depth systematic analysis of different machine learning methods used to predict graft outcomes among kidney transplant patients, and assess their usefulness as an aid to decision-making. METHODS A systemic review of machine learning methods used to predict graft outcomes among kidney transplant patients was carried out using a search of the Medline, the Cumulative Index to Nursing and Allied Health Literature, EMBASE, PsycINFO and Cochrane databases. RESULTS A total of 295 articles were identified and extracted. Of these, 18 met the inclusion criteria. Most of the studies were published in the United States after 2010. The population size used to develop the models varied from 80 to 92,844, and the number of features in the models ranged from 6 to 71. The most common machine learning methods used were artificial neural networks, decision trees and Bayesian belief networks. Most of the machine learning based predictive models predicted graft failure with high sensitivity and specificity. Only one machine learning based prediction model had modelled time-to-event (survival) information. Seven studies compared the predictive performance of machine learning models with traditional regression methods and the performance of machine learning methods was found to be mixed, when compared with traditional regression methods. CONCLUSION There was a wide variation in the size of the study population and the input variables used. However, the prediction accuracy provided mixed results when machine learning and traditional predictive methods are compared. Based on reported gains in predictive performance, machine learning has the potential to improve kidney transplant outcome prediction and aid medical decision making.
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Affiliation(s)
- Sameera Senanayake
- Australian Centre for Health Service Innovation, Queensland University of Technology, Australia.
| | - Nicole White
- Australian Centre for Health Service Innovation, Queensland University of Technology, Australia
| | - Nicholas Graves
- Australian Centre for Health Service Innovation, Queensland University of Technology, Australia
| | - Helen Healy
- Royal Brisbane Hospital for Women, Brisbane, Australia; School of Medicine, University of Queensland, Australia
| | - Keshwar Baboolal
- Royal Brisbane Hospital for Women, Brisbane, Australia; School of Medicine, University of Queensland, Australia
| | - Sanjeewa Kularatna
- Australian Centre for Health Service Innovation, Queensland University of Technology, Australia
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Feng JZ, Wang Y, Peng J, Sun MW, Zeng J, Jiang H. Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries. J Crit Care 2019; 54:110-116. [PMID: 31408805 DOI: 10.1016/j.jcrc.2019.08.010] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 07/03/2019] [Accepted: 08/02/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE To compare twenty-two machine learning (ML) models against logistic regression on survival prediction in severe traumatic brain injury (STBI) patients in a single center study. MATERIALS AND METHODS Data was collected from STBI patients admitted to the Sichuan Provincial People's Hospital between December 2009 and November 2011. Twenty-two machine learning (ML) models were tested, and their predictive performance compared with logistic regression (LR) model. Receiver operating characteristics (ROC), area under curve (AUC), accuracy, F-score, precision, recall and Decision Curve Analysis (DCA) were used as performance metrics. RESULTS A total of 117 patients were enrolled. AUC of all ML models ranged from 86.3% to 94%. AUC of LR was 83%, and accuracy was 88%. The AUC of Cubic SVM, Quadratic SVM and Linear SVM were higher than that of LR. The precision ratio of LR was 95% and recall ratio was 91%, both were lower than most ML models. The F-Score of LR was 0.93, which was only slightly better than that of Linear Discriminant and Quadratic Discriminant. CONCLUSIONS The twenty-two ML models selected have capabilities comparable to classical LR model for outcome prediction in STBI patients. Of these, Cubic SVM, Quadratic SVM, Linear SVM performed significantly better than LR.
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Affiliation(s)
- Jin-Zhou Feng
- Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China; Sino-Finnish Medical AI Research Center, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China.
| | - Yu Wang
- Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China; Sino-Finnish Medical AI Research Center, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China.
| | - Jin Peng
- Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China; Sino-Finnish Medical AI Research Center, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China; Department of Histology, Embryology and Neurobiology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No. 17, People's South Road, Chengdu, Sichuan, China.
| | - Ming-Wei Sun
- Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China.
| | - Jun Zeng
- Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China; Sino-Finnish Medical AI Research Center, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China.
| | - Hua Jiang
- Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China; Sino-Finnish Medical AI Research Center, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China.
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Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2017.01.012] [Citation(s) in RCA: 151] [Impact Index Per Article: 30.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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DuBay DA, Su Z, Morinelli TA, Baliga P, Rohan V, Bian J, Northrup D, Pilch N, Rao V, Srinivas TR, Mauldin PD, Taber DJ. Development and future deployment of a 5 years allograft survival model for kidney transplantation. Nephrology (Carlton) 2019; 24:855-862. [PMID: 30198104 DOI: 10.1111/nep.13488] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2018] [Indexed: 01/13/2023]
Abstract
AIM Identifying kidney transplant patients at highest risk for graft loss prior to loss may allow for effective interventions to improve 5 years survival. METHODS We performed a 10 years retrospective cohort study of adult kidney transplant recipients (n = 1747). We acquired data from electronic health records, United Network of Organ Sharing, social determinants of health, natural language processing data extraction, and real-time capture of dynamically evolving clinical data obtained within 1 year of transplant; from which we developed a 5 years graft survival model. RESULTS Total of 1439 met eligibility; 265 (18.4%) of them experienced graft loss by 5 years. Graft loss patients were characterized by: older age, being African-American, diabetic, unemployed, smokers, having marginal donor kidneys and cardiovascular comorbidities. Predictive dynamic variables included: low mean blood pressure, higher pulse pressures, higher heart rate, anaemia, lower estimated glomerular filtration rate peak, increased tacrolimus variability, rejection and readmissions. This Big Data analysis generated a 5 years graft loss model with an 82% predictive capacity, versus 66% using baseline United Network of Organ Sharing data alone. CONCLUSION Our analysis yielded a 5 years graft loss model demonstrating superior predictive capacity compared with United Network of Organ Sharing data alone, allowing post-transplant individualized risk-assessed care prior to transitioning back to community care.
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Affiliation(s)
- Derek A DuBay
- Department of Surgery, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Zemin Su
- Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Thomas A Morinelli
- Department of Surgery, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Prabhakar Baliga
- Department of Surgery, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Vinayak Rohan
- Department of Surgery, Medical University of South Carolina, Charleston, South Carolina, USA
| | - John Bian
- Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, South Carolina, USA
| | - David Northrup
- Office of the Chief Information Officer, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Nicole Pilch
- Department of Clinical Pharmacy and Outcomes Sciences, College of Pharmacy, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Vinaya Rao
- Department of Surgery, Medical University of South Carolina, Charleston, South Carolina, USA
| | | | - Patrick D Mauldin
- Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, South Carolina, USA
| | - David J Taber
- Department of Surgery, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Pharmacy, Ralph H Johnson VAMC, Charleston, South Carolina, USA
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Ganggayah MD, Taib NA, Har YC, Lio P, Dhillon SK. Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Med Inform Decis Mak 2019; 19:48. [PMID: 30902088 PMCID: PMC6431077 DOI: 10.1186/s12911-019-0801-4] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 03/18/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate. METHODS A large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis. RESULTS In terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis. CONCLUSION Interestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.
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Affiliation(s)
- Mogana Darshini Ganggayah
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Nur Aishah Taib
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Yip Cheng Har
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, England
| | - Sarinder Kaur Dhillon
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.
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Zhao J, Gu S, McDermaid A. Predicting outcomes of chronic kidney disease from EMR data based on Random Forest Regression. Math Biosci 2019; 310:24-30. [PMID: 30768948 DOI: 10.1016/j.mbs.2019.02.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 01/26/2019] [Accepted: 02/07/2019] [Indexed: 02/07/2023]
Abstract
Chronic kidney disease (CKD) is prevalent across the world, and kidney function is well defined by an estimated glomerular filtration rate (eGFR). The progression of kidney disease can be predicted if the future eGFR can be accurately estimated using predictive analytics. In this study, we developed and validated a prediction model of eGFR by data extracted from a regional health system. This dataset includes demographic, clinical and laboratory information from primary care clinics. The model was built using Random Forest regression and evaluated using Goodness-of-fit statistics and discrimination metrics. After data preprocessing, the patient cohort for model development and validation contained 61,740 patients. The final model included eGFR, age, gender, body mass index (BMI), obesity, hypertension, and diabetes, which achieved a mean coefficient of determination of 0.95. The estimated eGFRs were used to classify patients into CKD stages with high macro-averaged and micro-averaged metrics. In conclusion, a model using real-world electronic medical records (EMR) data can accurately predict future kidney functions and provide clinical decision support.
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Affiliation(s)
- Jing Zhao
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA; Sanford Research, Sioux Falls, SD 57104, USA.
| | - Shaopeng Gu
- Bioinformatics and Mathematical Biosciences Lab, Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57006, USA.
| | - Adam McDermaid
- Bioinformatics and Mathematical Biosciences Lab, Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57006, USA.
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A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave. BIOMED RESEARCH INTERNATIONAL 2018; 2018:2964816. [PMID: 30534557 PMCID: PMC6252205 DOI: 10.1155/2018/2964816] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 10/05/2018] [Accepted: 10/28/2018] [Indexed: 12/28/2022]
Abstract
Objective In this study, machine learning was utilized to classify and predict pulse wave of hypertensive group and healthy group and assess the risk of hypertension by observing the dynamic change of the pulse wave and provide an objective reference for clinical application of pulse diagnosis in traditional Chinese medicine (TCM). Method The basic information from 450 hypertensive cases and 479 healthy cases was collected by self-developed H20 questionnaires and pulse wave information was acquired by self-developed pulse diagnostic instrument (PDA-1). H20 questionnaires and pulse wave information were used as input variables to obtain different machine learning classification models of hypertension. This method was aimed at analyzing the influence of pulse wave on the accuracy and stability of machine learning model, as well as the feature contribution of hypertension model after removing noise by K-means. Result Compared with the classification results before removing noise, the accuracy and the area under the curve (AUC) had been improved. The accuracy rates of AdaBoost, Gradient Boosting, and Random Forest (RF) were 86.41%, 86.41%, and 85.33%, respectively. AUC were 0.86, 0.86, and 0.85, respectively. The maximum accuracy of SVM increased from 79.57% to 83.15%, and the AUC stability increased from 0.79 to 0.83. In addition, the features of importance on traditional statistics and machine learning were consistent. After removing noise, the features with large changes were h1/t1, w1/t, t, w2, h2, t1, and t5 in AdaBoost and Gradient Boosting (top10). The common variables for machine learning and traditional statistics were h1/t1, h5, t, Ad, BMI, and t2. Conclusion Pulse wave-based diagnostic method of hypertension has significant value in reference. In view of the feasibility of digital-pulse-wave diagnosis and dynamically evaluating hypertension, it provides the research direction and foundation for Chinese medicine in the dynamic evaluation of modern disease diagnosis and curative effect.
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Jones A, Costa AP, Pesevski A, McNicholas PD. Predicting hospital and emergency department utilization among community-dwelling older adults: Statistical and machine learning approaches. PLoS One 2018; 13:e0206662. [PMID: 30383850 PMCID: PMC6211724 DOI: 10.1371/journal.pone.0206662] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 10/10/2018] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE The objective of this study was to compare the performance of several commonly used machine learning methods to traditional statistical methods for predicting emergency department and hospital utilization among patients receiving publicly-funded home care services. STUDY DESIGN AND SETTING We conducted a population-based retrospective cohort study of publicly-funded home care recipients in the Hamilton-Niagara-Haldimand-Brant region of southern Ontario, Canada between 2014 and 2016. Gradient boosted trees, neural networks, and random forests were tested against two variations of logistic regression for predicting three outcomes related to emergency department and hospital utilization within six months of a comprehensive home care clinical assessment. Models were trained on data from years 2014 and 2015 and tested on data from 2016. Performance was compared using logarithmic score, Brier score, AUC, and diagnostic accuracy measures. RESULTS Gradient boosted trees achieved the best performance on all three outcomes. Gradient boosted trees provided small but statistically significant performance gains over both traditional methods on all three outcomes, while neural networks significantly outperformed logistic regression on two of three outcomes. However, sensitivity and specificity gains from using gradient boosted trees over logistic regression were only in the range of 1%-2% at several classification thresholds. CONCLUSION Gradient boosted trees and simple neural networks yielded small performance benefits over logistic regression for predicting emergency department and hospital utilization among patients receiving publicly-funded home care. However, the performance benefits were of negligible clinical importance.
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Affiliation(s)
- Aaron Jones
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- * E-mail:
| | - Andrew P. Costa
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Angelina Pesevski
- School of Computational Science and Engineering, McMaster University Hamilton, Ontario, Canada
| | - Paul D. McNicholas
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
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van Os HJA, Ramos LA, Hilbert A, van Leeuwen M, van Walderveen MAA, Kruyt ND, Dippel DWJ, Steyerberg EW, van der Schaaf IC, Lingsma HF, Schonewille WJ, Majoie CBLM, Olabarriaga SD, Zwinderman KH, Venema E, Marquering HA, Wermer MJH. Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms. Front Neurol 2018; 9:784. [PMID: 30319525 PMCID: PMC6167479 DOI: 10.3389/fneur.2018.00784] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 08/30/2018] [Indexed: 11/24/2022] Open
Abstract
Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ≥ 2b) and functional independence (modified Rankin Scale ≤2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed. Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53–0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77–0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88–0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00–0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge). Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.
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Affiliation(s)
| | - Lucas A Ramos
- Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands.,Department of Clinical Epidemiology and Biostatistics, University of Amsterdam, Amsterdam, Netherlands
| | - Adam Hilbert
- Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands
| | - Matthijs van Leeuwen
- Leiden Institute for Advanced Computer Sciences, Leiden University, Leiden, Netherlands
| | | | - Nyika D Kruyt
- Department of Neurology, Leiden University Medical Center, Leiden, Netherlands
| | | | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands.,Department of Public Health, Erasmus Medical Center, Rotterdam, Netherlands
| | | | - Hester F Lingsma
- Department of Public Health, Erasmus Medical Center, Rotterdam, Netherlands
| | | | - Charles B L M Majoie
- Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, Netherlands
| | - Silvia D Olabarriaga
- Department of Clinical Epidemiology and Biostatistics, University of Amsterdam, Amsterdam, Netherlands
| | - Koos H Zwinderman
- Department of Clinical Epidemiology and Biostatistics, University of Amsterdam, Amsterdam, Netherlands
| | - Esmee Venema
- Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands.,Department of Public Health, Erasmus Medical Center, Rotterdam, Netherlands
| | - Henk A Marquering
- Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands
| | - Marieke J H Wermer
- Department of Neurology, Leiden University Medical Center, Leiden, Netherlands
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Oeyen S, Vermeulen K, Benoit D, Annemans L, Decruyenaere J. Development of a prediction model for long-term quality of life in critically ill patients. J Crit Care 2017; 43:133-138. [PMID: 28892669 DOI: 10.1016/j.jcrc.2017.09.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 08/22/2017] [Accepted: 09/02/2017] [Indexed: 11/25/2022]
Abstract
PURPOSE We developed a prediction model for quality of life (QOL) 1 year after intensive care unit (ICU) discharge based upon data available at the first ICU day to improve decision-making. METHODS The database of a 1-year prospective study concerning long-term outcome and QOL (assessed by EuroQol-5D) in critically ill adult patients consecutively admitted to the ICU of a university hospital was used. Cases with missing data were excluded. Utility indices at baseline (UIb) and at 1 year (UI1y) were surrogates for QOL. For 1-year non-survivors UI1y was set at zero. The grouped lasso technique selected the most important variables in the prediction model. R2 and adjusted R2 were calculated. RESULTS 1831 of 1953 cases (93.8%) were complete. UI1y depended significantly on: UIb (P<0.001); solid tumor (P<0.001); age (P<0.001); activity of daily living (P<0.001); imaging (P<0.001); APACHE II-score (P=0.001); ≥80 years (P=0.001); mechanical ventilation (P=0.006); hematological patient (P=0.007); SOFA-score (P=0.008); tracheotomy (P=0.018); admission diagnosis surgical P<0.001 (versus medical); and comorbidity (P=0.049). Only baseline health status and surgical patients were positively associated with UI1y. R2 was 0.3875 and adjusted R2 0.3807. CONCLUSION Although only 40% of variability in long-term QOL could be explained, this prediction model can be helpful in decision-making.
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Affiliation(s)
- Sandra Oeyen
- Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Department of Intensive Care, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium.
| | - Karel Vermeulen
- Faculty of Bioscience Engineering, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
| | - Dominique Benoit
- Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Department of Intensive Care, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium.
| | - Lieven Annemans
- Faculty of Medicine and Health Sciences, Department of Public Health, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium.
| | - Johan Decruyenaere
- Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Department of Intensive Care, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium.
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Yoo KD, Noh J, Lee H, Kim DK, Lim CS, Kim YH, Lee JP, Kim G, Kim YS. A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study. Sci Rep 2017; 7:8904. [PMID: 28827646 PMCID: PMC5567098 DOI: 10.1038/s41598-017-08008-8] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 07/07/2017] [Indexed: 01/20/2023] Open
Abstract
Accurate prediction of graft survival after kidney transplant is limited by the complexity and heterogeneity of risk factors influencing allograft survival. In this study, we applied machine learning methods, in combination with survival statistics, to build new prediction models of graft survival that included immunological factors, as well as known recipient and donor variables. Graft survival was estimated from a retrospective analysis of the data from a multicenter cohort of 3,117 kidney transplant recipients. We evaluated the predictive power of ensemble learning algorithms (survival decision tree, bagging, random forest, and ridge and lasso) and compared outcomes to those of conventional models (decision tree and Cox regression). Using a conventional decision tree model, the 3-month serum creatinine level post-transplant (cut-off, 1.65 mg/dl) predicted a graft failure rate of 77.8% (index of concordance, 0.71). Using a survival decision tree model increased the index of concordance to 0.80, with the episode of acute rejection during the first year post-transplant being associated with a 4.27-fold increase in the risk of graft failure. Our study revealed that early acute rejection in the first year is associated with a substantially increased risk of graft failure. Machine learning methods may provide versatile and feasible tools for forecasting graft survival.
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Affiliation(s)
- Kyung Don Yoo
- Department of Internal Medicine, Dongguk University College of Medicine, Gyeongju, Korea
| | - Junhyug Noh
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Korea
| | - Hajeong Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Dong Ki Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Chun Soo Lim
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Korea
| | - Young Hoon Kim
- Department of Surgery, College of Medicine, Ulsan University, Asan Medical Center, Seoul, Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Korea
| | - Gunhee Kim
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Korea.
| | - Yon Su Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
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In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches. BIOMED RESEARCH INTERNATIONAL 2016; 2016:2375268. [PMID: 27579307 PMCID: PMC4992803 DOI: 10.1155/2016/2375268] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Revised: 06/08/2016] [Accepted: 06/19/2016] [Indexed: 11/17/2022]
Abstract
Gamma-aminobutyric acid type-A receptors (GABAARs) belong to multisubunit membrane spanning ligand-gated ion channels (LGICs) which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category prediction of GABAARs just from the protein amino acid sequence would be very helpful for the recognition and research of novel receptors. Based on the proteins' physicochemical properties, amino acids composition and position, a GABAAR classifier was first constructed using a 188-dimensional (188D) algorithm at 90% cd-hit identity and compared with pseudo-amino acid composition (PseAAC) and ProtrWeb web-based algorithms for human GABAAR proteins. Then, four classifiers including gradient boosting decision tree (GBDT), random forest (RF), a library for support vector machine (libSVM), and k-nearest neighbor (k-NN) were compared on the dataset at cd-hit 40% low identity. This work obtained the highest correctly classified rate at 96.8% and the highest specificity at 99.29%. But the values of sensitivity, accuracy, and Matthew's correlation coefficient were a little lower than those of PseAAC and ProtrWeb; GBDT and libSVM can make a little better performance than RF and k-NN at the second dataset. In conclusion, a GABAAR classifier was successfully constructed using only the protein sequence information.
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