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Tao X, Jiang M, Liu Y, Hu Q, Zhu B, Hu J, Guo W, Wu X, Xiong Y, Shi X, Zhang X, Han X, Li W, Tong R, Long E. Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms. Sci Rep 2023; 13:16437. [PMID: 37777593 PMCID: PMC10543442 DOI: 10.1038/s41598-023-43240-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/21/2023] [Indexed: 10/02/2023] Open
Abstract
Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020. After data preprocessing, data inputting, data sampling, and feature screening, 16 kinds of machine learning methods were used to construct prediction models, and 5 prediction models with the best prediction performance were screened respectively. A total of 100,000 cases were included to establish the FBG model, and 2,169 cases were established to establish the HbA1c model. The best prediction model both of FBG and HbA1c finally obtained are realized by ensemble learning and modified random forest inputting, the AUC values are 0.819 and 0.970, respectively. The most important indicators of the FBG and HbA1c prediction model were FBG and HbA1c. Medication compliance, follow-up outcome, dietary habits, BMI, and waist circumference also had a greater impact on FBG levels. The prediction accuracy of the models of the two blood glucose control indicators is high and has certain clinical applicability.HbA1c and FBG are mutually important predictors, and there is a close relationship between them.
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Affiliation(s)
- Xue Tao
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Min Jiang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610044, Sichuan, China
| | - Yumeng Liu
- Department of Pharmacy, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Qi Hu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, Sichuan, China
| | - Baoqiang Zhu
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Jiaqiang Hu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Wenmei Guo
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Xingwei Wu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Yu Xiong
- Institute of Materia Medica, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, 100050, China
| | - Xia Shi
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Xueli Zhang
- Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China
| | - Xu Han
- Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China
| | - Wenyuan Li
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Rongsheng Tong
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Enwu Long
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China.
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Mistry S, Riches NO, Gouripeddi R, Facelli JC. Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review. Artif Intell Med 2023; 135:102461. [PMID: 36628796 PMCID: PMC9834645 DOI: 10.1016/j.artmed.2022.102461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 10/06/2022] [Accepted: 11/23/2022] [Indexed: 12/03/2022]
Abstract
BACKGROUND Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed. OBJECTIVE We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction. METHODS We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes etiology on September 19th, 2022. Exposures were classified into specific external, general external, or internal exposures. We reviewed machine learning and data mining methods and characterized the scope of environmental exposures studied in the etiology of general diabetes, type 1 diabetes, type 2 diabetes, and other types of diabetes. RESULTS We identified 44 articles for inclusion. Specific external exposures were the most common exposures studied, and supervised models were the most common methods used. Well-established specific external exposures of low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes. DISCUSSION The use of machine learning and data mining methods to elucidate environmental triggers of diabetes was largely limited to well-established risk factors identified using easily explainable and interpretable models. Future studies should seek to leverage machine learning and data mining to explore the temporality and co-occurrence of multiple exposures and further evaluate the role of general external and internal exposures in diabetes etiology.
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Affiliation(s)
- Sejal Mistry
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, UT, USA
| | - Naomi O Riches
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, UT, USA; Department of Obstetrics and Gynecology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Ramkiran Gouripeddi
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, UT, USA; Clinical and Translational Science Institute, University of Utah, Salt Lake City, UT, USA
| | - Julio C Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, UT, USA; Clinical and Translational Science Institute, University of Utah, Salt Lake City, UT, USA.
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He J, Li J, Jiang S, Cheng W, Jiang J, Xu Y, Yang J, Zhou X, Chai C, Wu C. Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation. Front Public Health 2022; 10:967681. [PMID: 36091522 PMCID: PMC9452878 DOI: 10.3389/fpubh.2022.967681] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/02/2022] [Indexed: 01/25/2023] Open
Abstract
Background Continuously growing of HIV incidence among men who have sex with men (MSM), as well as the low rate of HIV testing of MSM in China, demonstrates a need for innovative strategies to improve the implementation of HIV prevention. The use of machine learning algorithms is an increasing tendency in disease diagnosis prediction. We aimed to develop and validate machine learning models in predicting HIV infection among MSM that can identify individuals at increased risk of HIV acquisition for transmission-reduction interventions. Methods We extracted data from MSM sentinel surveillance in Zhejiang province from 2018 to 2020. Univariate logistic regression was used to select significant variables in 2018-2019 data (P < 0.05). After data processing and feature selection, we divided the model development data into two groups by stratified random sampling: training data (70%) and testing data (30%). The Synthetic Minority Oversampling Technique (SMOTE) was applied to solve the problem of unbalanced data. The evaluation metrics of model performance were comprised of accuracy, precision, recall, F-measure, and the area under the receiver operating characteristic curve (AUC). Then, we explored three commonly-used machine learning algorithms to compare with logistic regression (LR), including decision tree (DT), support vector machines (SVM), and random forest (RF). Finally, the four models were validated prospectively with 2020 data from Zhejiang province. Results A total of 6,346 MSM were included in model development data, 372 of whom were diagnosed with HIV. In feature selection, 12 variables were selected as model predicting indicators. Compared with LR, the algorithms of DT, SVM, and RF improved the classification prediction performance in SMOTE-processed data, with the AUC of 0.778, 0.856, 0.887, and 0.942, respectively. RF was the best-performing algorithm (accuracy = 0.871, precision = 0.960, recall = 0.775, F-measure = 0.858, and AUC = 0.942). And the RF model still performed well on prospective validation (AUC = 0.846). Conclusion Machine learning models are substantially better than conventional LR model and RF should be considered in prediction tools of HIV infection in Chinese MSM. Further studies are needed to optimize and promote these algorithms and evaluate their impact on HIV prevention of MSM.
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Affiliation(s)
- Jiajin He
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Jinhua Li
- School of Software Technology, Zhejiang University, Ningbo, China
| | - Siqing Jiang
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Cheng
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Jun Jiang
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Yun Xu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Jiezhe Yang
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Xin Zhou
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Chengliang Chai
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China,*Correspondence: Chengliang Chai
| | - Chao Wu
- School of Public Affairs, Zhejiang University, Hangzhou, China,Chao Wu
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Alshehri S, Alqarni M, Namazi NI, Naguib IA, Venkatesan K, Mosaad YO, Pishnamazi M, Alsubaiyel AM, Abourehab MAS. Design of predictive model to optimize the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug. Sci Rep 2022; 12:13106. [PMID: 35907929 PMCID: PMC9338975 DOI: 10.1038/s41598-022-17350-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/25/2022] [Indexed: 11/09/2022] Open
Abstract
These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO2). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO2 through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E-09, 9.71E-09, and 3.78E-11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.
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Affiliation(s)
- Sameer Alshehri
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
| | - Mohammed Alqarni
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Nader Ibrahim Namazi
- Pharmaceutics and Pharmaceutical Technology Department, College of Pharmacy, Taibah University, Al Madinah Al Munawarah, 30001, Saudi Arabia
| | - Ibrahim A Naguib
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Kumar Venkatesan
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Khalid University, Abha, 62529, Kingdom of Saudi Arabia
| | - Yasser O Mosaad
- Department of Pharmacy Practice and Clinical Pharmacy, Faculty Pharmacy, Future Unibversity in Egypt, New Cairo, 11835, Egypt
| | - Mahboubeh Pishnamazi
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam. .,The Faculty of Pharmacy, Duy Tan University, Da Nang, 550000, Viet Nam.
| | - Amal M Alsubaiyel
- Department of Pharmaceutics, College of Pharmacy, Qassim University, Buraidah, 52571, Saudi Arabia
| | - Mohammed A S Abourehab
- Department of Pharmaceutics, Faculty of Pharmacy, Umm Al-Qura University, Makkah, 21955, Saudi Arabia.,Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Minia University, Minia, 61519, Egypt
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Liu Q, Zhou Q, He Y, Zou J, Guo Y, Yan Y. Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults. J Pers Med 2022; 12:jpm12071055. [PMID: 35887552 PMCID: PMC9324396 DOI: 10.3390/jpm12071055] [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: 04/10/2022] [Revised: 06/06/2022] [Accepted: 06/23/2022] [Indexed: 11/18/2022] Open
Abstract
Identifying people with a high risk of developing diabetes among those with prediabetes may facilitate the implementation of a targeted lifestyle and pharmacological interventions. We aimed to establish machine learning models based on demographic and clinical characteristics to predict the risk of incident diabetes. We used data from the free medical examination service project for elderly people who were 65 years or older to develop logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) machine learning models for the follow-up results of 2019 and 2020 and performed internal validation. The receiver operating characteristic (ROC), sensitivity, specificity, accuracy, and F1 score were used to select the model with better performance. The average annual progression rate to diabetes in prediabetic elderly people was 14.21%. Each model was trained using eight features and one outcome variable from 9607 prediabetic individuals, and the performance of the models was assessed in 2402 prediabetes patients. The predictive ability of four models in the first year was better than in the second year. The XGBoost model performed relatively efficiently (ROC: 0.6742 for 2019 and 0.6707 for 2020). We established and compared four machine learning models to predict the risk of progression from prediabetes to diabetes. Although there was little difference in the performance of the four models, the XGBoost model had a relatively good ROC value, which might perform well in future exploration in this field.
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Affiliation(s)
- Qing Liu
- Department of Epidemiology, School of Public Health, Wuhan University, Wuhan 430071, China; (Q.L.); (Q.Z.)
| | - Qing Zhou
- Department of Epidemiology, School of Public Health, Wuhan University, Wuhan 430071, China; (Q.L.); (Q.Z.)
| | - Yifeng He
- School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; (Y.H.); (J.Z.)
| | - Jingui Zou
- School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; (Y.H.); (J.Z.)
| | - Yan Guo
- Wuhan Center for Disease Control and Prevention, Wuhan 430015, China;
| | - Yaqiong Yan
- Wuhan Center for Disease Control and Prevention, Wuhan 430015, China;
- Correspondence:
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Liu Q, Zhang M, He Y, Zhang L, Zou J, Yan Y, Guo Y. Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques. J Pers Med 2022; 12:jpm12060905. [PMID: 35743691 PMCID: PMC9224915 DOI: 10.3390/jpm12060905] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/21/2022] [Accepted: 05/27/2022] [Indexed: 02/04/2023] Open
Abstract
Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.
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Affiliation(s)
- Qing Liu
- Department of Epidemiology, School of Public Health, Wuhan University, Wuhan 430071, China; (Q.L.); (M.Z.)
| | - Miao Zhang
- Department of Epidemiology, School of Public Health, Wuhan University, Wuhan 430071, China; (Q.L.); (M.Z.)
| | - Yifeng He
- School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; (Y.H.); (J.Z.)
| | - Lei Zhang
- School of Mathematics and Statistics, Wuhan University, Wuhan 430070, China;
| | - Jingui Zou
- School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; (Y.H.); (J.Z.)
| | - Yaqiong Yan
- Wuhan Center for Disease Control and Prevention, Wuhan 430015, China;
| | - Yan Guo
- Wuhan Center for Disease Control and Prevention, Wuhan 430015, China;
- Correspondence:
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Development of multiple machine-learning computational techniques for optimization of heterogenous catalytic biodiesel production from waste vegetable oil. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.103843] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6696041. [PMID: 33860053 PMCID: PMC8024075 DOI: 10.1155/2021/6696041] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 03/17/2021] [Indexed: 02/07/2023]
Abstract
Objective To establish a machine learning model for identifying patients coinfected with hepatitis B virus (HBV) and human immunodeficiency virus (HIV) through two sexual transmission routes in Jiangsu, China. Methods A total of 14197 HIV cases transmitted by homosexual and heterosexual routes were recruited. After data processing, 12469 cases (HIV and HBV, 1033; HIV, 11436) were left for further analysis, including 7849 cases with homosexual transmission and 4620 cases with heterosexual transmission. Univariate logistic regression was used to select variables with significant P value and odds ratio for multivariable analysis. In homosexual transmission and heterosexual transmission groups, 10 and 6 variables were selected, respectively. For identifying HIV individuals coinfected with HBV, a machine learning model was constructed with four algorithms, including Decision Tree, Random Forest, AdaBoost with decision tree (AdaBoost), and extreme gradient boosting decision tree (XGBoost). The detective value of each variable was calculated using the optimal machine learning algorithm. Results AdaBoost algorithm showed the highest efficiency in both transmission groups (homosexual transmission group: accuracy = 0.928, precision = 0.915, recall = 0.944, F − 1 = 0.930, and AUC = 0.96; heterosexual transmission group: accuracy = 0.892, precision = 0.881, recall = 0.905, F − 1 = 0.893, and AUC = 0.98). Calculated by AdaBoost algorithm, the detective value of PLA was the highest in homosexual transmission group, followed by CR, AST, HB, ALT, TBIL, leucocyte, age, marital status, and treatment condition; in the heterosexual transmission group, the detective value of PLA was the highest (consistent with the condition in the homosexual group), followed by ALT, AST, TBIL, leucocyte, and symptom severity. Conclusions The univariate logistics regression combined with the AdaBoost algorithm could accurately screen the risk factors of HBV in HIV coinfection without invasive testing. Further studies are needed to evaluate the utility and feasibility of this model in various settings.
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Nan C, Schmidt O, Lindner R, Ilgin Y, Schultz T, Hinsch Gylvin L, Bleecker ER. German regional variation of acute and high oral corticosteroid use for asthma. J Asthma 2021; 59:791-800. [PMID: 33492176 DOI: 10.1080/02770903.2021.1878532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE: To improve understanding of real-world asthma treatment and inform physician education, we evaluated regional variation in asthma prevalence and oral corticosteroid (OCS) use across Germany. METHODS: We developed a machine learning gradient-boosted tree model with IMS® Disease Analyzer electronic medical records, which cover 3% of German patients. This model had a 91% accuracy in predicting the presence of asthma and chronic obstructive pulmonary disease. We applied the model to the IMS® Longitudinal Prescription database, with 82% national coverage, to classify patients receiving treatment for airflow obstruction from October 2017-September 2018 in 63 regions in Germany. RESULTS: Of 2.4 million individuals under statutory health insurance predicted to have asthma, 13.7%, 18.7%, 36.5%, 29.4%, and 1.7% received treatment classified as Global Initiative for Asthma (GINA) Steps 1, 2, 3, 4, and 5, respectively. Approximately 7-15% of those at GINA Steps 1-4 and 35% at Step 5 treatment received ≥1 acute OCS prescription (duration <10 days). Of patients receiving GINA Steps 1-4 and Step 5 treatments, 1-3% and 86%, respectively, received ≥1 high-dosage OCS prescription. Cumulative OCS dosage and percentages of patients receiving OCS differed substantially across regions, and regions with lower OCS use had greater use of biologic therapies. CONCLUSIONS: Both acute and high OCS use varied regionally across Germany, with overall use suggesting patients are considerable risk of adverse effects and long-term health consequences. Supplemental data for this article can be accessed at publisher's website.
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