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Tsai CH, Shih DH, Tu JH, Wu TW, Tsai MG, Shih MH. Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients. J Clin Med 2024; 13:2283. [PMID: 38673554 PMCID: PMC11051209 DOI: 10.3390/jcm13082283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 03/27/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Background: The increase in the global population of hemodialysis patients is linked to aging demographics and the prevalence of conditions such as arterial hypertension and diabetes mellitus. While previous research in hemodialysis has mainly focused on mortality predictions, there is a gap in studies targeting short-term hospitalization predictions using detailed, monthly blood test data. Methods: This study employs advanced data preprocessing and machine learning techniques to predict hospitalizations within a 30-day period among hemodialysis patients. Initial steps include employing K-Nearest Neighbor (KNN) imputation to address missing data and using the Synthesized Minority Oversampling Technique (SMOTE) to ensure data balance. The study then applies a Support Vector Machine (SVM) algorithm for the predictive analysis, with an additional enhancement through ensemble learning techniques, in order to improve prediction accuracy. Results: The application of SVM in predicting hospitalizations within a 30-day period among hemodialysis patients resulted in an impressive accuracy rate of 93%. This accuracy rate further improved to 96% upon incorporating ensemble learning methods, demonstrating the efficacy of the chosen machine learning approach in this context. Conclusions: This study highlights the potential of utilizing machine learning to predict hospital readmissions within a 30-day period among hemodialysis patients based on monthly blood test data. It represents a significant leap towards precision medicine and personalized healthcare for this patient group, suggesting a paradigm shift in patient care through the proactive identification of hospitalization risks.
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Affiliation(s)
- Cheng-Han Tsai
- Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi City 62102, Taiwan or
- Department of Emergency Medicine, Chiayi Branch, Taichung Veteran’s General Hospital, Chiayi City 60090, Taiwan
| | - Dong-Her Shih
- Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan;
| | - Jue-Hong Tu
- Department of Nephrology, St. Joseph’s Hospital, Yunlin 63241, Taiwan; (J.-H.T.); (M.-G.T.)
| | - Ting-Wei Wu
- Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan;
| | - Ming-Guei Tsai
- Department of Nephrology, St. Joseph’s Hospital, Yunlin 63241, Taiwan; (J.-H.T.); (M.-G.T.)
| | - Ming-Hung Shih
- Department of Electrical and Computer Engineering, Iowa State University, 2520 Osborn Drive, Ames, IA 50011, USA;
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Rahimi M, Afrash MR, Shadnia S, Mostafazadeh B, Evini PET, Bardsiri MS, Ramezani M. Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms. BMC Med Inform Decis Mak 2024; 24:38. [PMID: 38321428 PMCID: PMC10845715 DOI: 10.1186/s12911-024-02443-0] [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: 09/18/2023] [Accepted: 01/28/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Hemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. Despite its effectiveness, there needs to be more research on this method precisely, with most studies focusing on specific poisoning. This study aims to bridge the existing knowledge gap by developing a machine-learning prediction model for forecasting the prognosis of the poisoned patient undergoing hemodialysis. METHODS Using a registry database from 2016 to 2022, this study conducted a retrospective cohort study at Loghman Hakim Hospital. First, the relief feature selection algorithm was used to identify the most important variables influencing the prognosis of poisoned patients undergoing hemodialysis. Second, four machine learning algorithms, including extreme gradient boosting (XGBoost), histgradient boosting (HGB), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost), were trained to construct predictive models for predicting the prognosis of poisoned patients undergoing hemodialysis. Finally, the performance of paired feature selection and machine learning (ML) algorithm were evaluated to select the best models using five evaluation metrics including accuracy, sensitivity, specificity the area under the curve (AUC), and f1-score. RESULT The study comprised 980 patients in total. The experimental results showed that ten variables had a significant influence on prognosis outcomes including age, intubation, acidity (PH), previous medical history, bicarbonate (HCO3), Glasgow coma scale (GCS), intensive care unit (ICU) admission, acute kidney injury, and potassium. Out of the four models evaluated, the HGB classifier stood out with superior results on the test dataset. It achieved an impressive mean classification accuracy of 94.8%, a mean specificity of 93.5 a mean sensitivity of 94%, a mean F-score of 89.2%, and a mean receiver operating characteristic (ROC) of 92%. CONCLUSION ML-based predictive models can predict the prognosis of poisoned patients undergoing hemodialysis with high performance. The developed ML models demonstrate valuable potential for providing frontline clinicians with data-driven, evidence-based tools to guide time-sensitive prognosis evaluations and care decisions for poisoned patients in need of hemodialysis. Further large-scale multi-center studies are warranted to validate the efficacy of these models across diverse populations.
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Affiliation(s)
- Mitra Rahimi
- Toxicological Research Center, Excellence Center & Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Afrash
- Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Shahin Shadnia
- Toxicological Research Center, Excellence Center & Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Babak Mostafazadeh
- Toxicological Research Center, Excellence Center & Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Peyman Erfan Talab Evini
- Toxicological Research Center, Excellence Center & Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohadeseh Sarbaz Bardsiri
- Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Clinical Toxicology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Maral Ramezani
- Department of Pharmacology, School of Medicine, Arak University of Medical Sciences, Arak, Iran.
- Traditional and Complementary Medicine Research Center, Arak University of Medical Sciences, Arak, Iran.
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Takkavatakarn K, Oh W, Cheng E, Nadkarni GN, Chan L. Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4. BMC Nephrol 2023; 24:376. [PMID: 38114923 PMCID: PMC10731874 DOI: 10.1186/s12882-023-03424-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
INTRODUCTION End-stage kidney disease (ESKD) is associated with increased morbidity and mortality. Identifying patients with stage 4 CKD (CKD4) at risk of rapid progression to ESKD remains challenging. Accurate prediction of CKD4 progression can improve patient outcomes by improving advanced care planning and optimizing healthcare resource allocation. METHODS We obtained electronic health record data from patients with CKD4 in a large health system between January 1, 2006, and December 31, 2016. We developed and validated four models, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network (ANN), to predict ESKD at 3 years. We utilized area under the receiver operating characteristic curve (AUROC) to evaluate model performances and utilized Shapley additive explanation (SHAP) values and plots to define feature dependence of the best performance model. RESULTS We included 3,160 patients with CKD4. ESKD was observed in 538 patients (21%). All approaches had similar AUROCs; ANN yielded the highest AUROC (0.77; 95%CI 0.75 to 0.79) and LASSO regression (0.77; 95%CI 0.75 to 0.79), followed by random forest (0.76; 95% CI 0.74 to 0.79), and XGBoost (0.76; 95% CI 0.74 to 0.78). CONCLUSIONS We developed and validated several models for near-term prediction of kidney failure in CKD4. ANN, random forest, and XGBoost demonstrated similar predictive performances. Using this suite of models, interventions can be customized based on risk, and population health and resources appropriately allocated.
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Affiliation(s)
- Kullaya Takkavatakarn
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand
| | - Wonsuk Oh
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ella Cheng
- The Cooper Union for the Advancement of Science and Art, New York, NY, USA
| | - Girish N Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Lee WT, Fang YW, Chang WS, Hsiao KY, Shia BC, Chen M, Tsai MH. Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients. Sci Rep 2023; 13:21453. [PMID: 38052875 PMCID: PMC10698192 DOI: 10.1038/s41598-023-48905-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 12/01/2023] [Indexed: 12/07/2023] Open
Abstract
Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under-curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.
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Affiliation(s)
- Wen-Teng Lee
- Division of Nephrology, Department of Internal Medicine, Shin-Kong Wu Ho-Su Memorial Hospital, No. 95, Wen-Chang Rd, Shih-Lin Dist., Taipei, 11101, Taiwan
| | - Yu-Wei Fang
- Division of Nephrology, Department of Internal Medicine, Shin-Kong Wu Ho-Su Memorial Hospital, No. 95, Wen-Chang Rd, Shih-Lin Dist., Taipei, 11101, Taiwan
- Department of Medicine, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan
| | - Wei-Shan Chang
- Artificial Intelligence Development Center, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist, New Taipei City, 24205, Taiwan
| | - Kai-Yuan Hsiao
- Artificial Intelligence Development Center, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist, New Taipei City, 24205, Taiwan
| | - Ben-Chang Shia
- Artificial Intelligence Development Center, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist, New Taipei City, 24205, Taiwan
| | - Mingchih Chen
- Artificial Intelligence Development Center, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan.
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist, New Taipei City, 24205, Taiwan.
| | - Ming-Hsien Tsai
- Division of Nephrology, Department of Internal Medicine, Shin-Kong Wu Ho-Su Memorial Hospital, No. 95, Wen-Chang Rd, Shih-Lin Dist., Taipei, 11101, Taiwan.
- Department of Medicine, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan.
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Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol 2023; 36:1101-1117. [PMID: 36786976 PMCID: PMC10227138 DOI: 10.1007/s40620-023-01573-4] [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: 08/06/2022] [Accepted: 01/01/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVES In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management. METHODS We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. RESULTS From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context. CONCLUSIONS Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.
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Rankin S, Han L, Scherzer R, Tenney S, Keating M, Genberg K, Rahn M, Wilkins K, Shlipak M, Estrella M. A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation. KIDNEY360 2022; 3:1556-1565. [PMID: 36245665 PMCID: PMC9528387 DOI: 10.34067/kid.0007012021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 07/15/2022] [Indexed: 11/27/2022]
Abstract
BackgroundThe first 90 days after dialysis initiation are associated with high morbidity and mortality in end-stage kidney disease (ESKD) patients. A machine learning–based tool for predicting mortality could inform patient-clinician shared decision making on whether to initiate dialysis or pursue medical management. We used the eXtreme Gradient Boosting (XGBoost) algorithm to predict mortality in the first 90 days after dialysis initiation in a nationally representative population from the United States Renal Data System.MethodsA cohort of adults initiating dialysis between 2008–2017 were studied for outcome of death within 90 days of dialysis initiation. The study dataset included 188 candidate predictors prognostic of early mortality that were known on or before the first day of dialysis and was partitioned into training (70%) and testing (30%) subsets. XGBoost modeling used a complete-case set and a dataset obtained from multiple imputation. Model performance was evaluated by c-statistics overall and stratified by subgroups of age, sex, race, and dialysis modality.ResultsThe analysis included 1,150,195 patients with ESKD, of whom 86,083 (8%) died in the first 90 days after dialysis initiation. The XGBoost models discriminated mortality risk in the nonimputed (c=0.826, 95% CI, 0.823 to 0.828) and imputed (c=0.827, 95% CI, 0.823 to 0.827) models and performed well across nearly every subgroup (race, age, sex, and dialysis modality) evaluated (c>0.75). Across predicted risk thresholds of 10%–50%, higher risk thresholds showed declining sensitivity (0.69–0.04) with improving specificity (0.79–0.99); similarly, positive likelihood ratio was highest at the 40% threshold, whereas the negative likelihood ratio was lowest at the 10% threshold. After calibration using isotonic regression, the model accurately estimated the probability of mortality across all ranges of predicted risk.ConclusionsThe XGBoost-based model developed in this study discriminated risk of early mortality after dialysis initiation with excellent calibration and performed well across key subgroups.
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AIM in Hemodialysis. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Yang CH, Chen YS, Moi SH, Chen JB, Wang L, Chuang LY. Machine learning approaches for the mortality risk assessment of patients undergoing hemodialysis. Ther Adv Chronic Dis 2022; 13:20406223221119617. [PMID: 36062293 PMCID: PMC9434675 DOI: 10.1177/20406223221119617] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 07/27/2022] [Indexed: 11/15/2022] Open
Abstract
Introduction: Mortality is a major primary endpoint for long-term hemodialysis (HD)
patients. The clinical status of HD patients generally relies on
longitudinal clinical observations such as monthly laboratory examinations
and physical examinations. Methods: A total of 829 HD patients who met the inclusion criteria were analyzed. All
patients were tracked from January 2009 to December 2013. Taken together,
this study performed full-adjusted-Cox proportional hazards (CoxPH),
stepwise-CoxPH, random survival forest (RSF)-CoxPH, and whale optimization
algorithm (WOA)-CoxPH model for the all-cause mortality risk assessment in
HD patients. The model performance between proposed selections of CoxPH
models were evaluated using concordance index. Results: The WOA-CoxPH model obtained the highest concordance index compared with
RSF-CoxPH and typical selection CoxPH model. The eight significant
parameters obtained from the WOA-CoxPH model, including age, diabetes
mellitus (DM), hemoglobin (Hb), albumin, creatinine (Cr), potassium (K),
Kt/V, and cardiothoracic ratio, have also showed significant survival
difference between low- and high-risk characteristics in single-factor
analysis. By integrating the risk characteristics of each single factor,
patients who obtained seven or more risk characteristics of eight selected
parameters were dichotomized as high-risk subgroup, and remaining is
considered as low-risk subgroup. The integrated low- and high-risk subgroup
showed greater discrepancy compared with each single risk factor selected by
WOA-CoxPH model. Conclusion: The study findings revealed WOA-CoxPH model could provide better risk
assessment performance compared with RSF-CoxPH and typical selection CoxPH
model in the HD patients. In summary, patients who had seven or more risk
characteristics of eight selected parameters were at potentially increased
risk of all-cause mortality in HD population.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Information Management, Tainan University of Technology, Tainan
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung
- Biomedical Engineering, Kaohsiung Medical University, Kaohsiung
- School of Dentistry, Kaohsiung Medical University, Kaohsiung
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung
| | - Yin-Syuan Chen
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung
| | - Sin-Hua Moi
- Center of Cancer Program Development, E-Da Cancer Hospital, I-Shou University, Kaohsiung 82445
| | - Jin-Bor Chen
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301
| | - Lin Wang
- Department of Nephrology, Dalian University Affiliated Xinhua Hospital, Dalian, 116001, China
| | - Li-Yeh Chuang
- Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84004
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Bbosa FF, Nabukenya J, Nabende P, Wesonga R. On the goodness of fit of parametric and non-parametric data mining techniques: the case of malaria incidence thresholds in Uganda. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00551-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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AIM in Hemodialysis. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_254-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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