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For: Cui S, Wang D, Wang Y, Yu PW, Jin Y. An improved support vector machine-based diabetic readmission prediction. Comput Methods Programs Biomed 2018;166:123-35. [PMID: 30415712 DOI: 10.1016/j.cmpb.2018.10.012] [Cited by in Crossref: 40] [Cited by in F6Publishing: 14] [Article Influence: 10.0] [Reference Citation Analysis]
Number Citing Articles
1 Cai B, Hao K, Wang Z, Yang C, Kong X, Liu Z, Ji R, Liu Y. Data-driven early fault diagnostic methodology of permanent magnet synchronous motor. Expert Systems with Applications 2021;177:115000. [DOI: 10.1016/j.eswa.2021.115000] [Cited by in Crossref: 16] [Cited by in F6Publishing: 7] [Article Influence: 16.0] [Reference Citation Analysis]
2 Shao L, You Y, Du H, Fu D. Classification of ADHD with fMRI data and multi-objective optimization. Comput Methods Programs Biomed 2020;196:105676. [PMID: 32791440 DOI: 10.1016/j.cmpb.2020.105676] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
3 Camerlingo N, Vettoretti M, Del Favero S, Facchinetti A, Choudhary P, Sparacino G; Hypo-RESOLVE Consortium. Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach. Comput Methods Programs Biomed 2022;221:106862. [PMID: 35597208 DOI: 10.1016/j.cmpb.2022.106862] [Reference Citation Analysis]
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5 Pettit RW, Fullem R, Cheng C, Amos CI. Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerg Top Life Sci 2021:ETLS20210246. [PMID: 34927670 DOI: 10.1042/ETLS20210246] [Reference Citation Analysis]
6 Mohamed I. Prediction of Chronic Obstructive Pulmonary Disease Stages Using Machine Learning Algorithms: . International Journal of Decision Support System Technology 2022;14:1-13. [DOI: 10.4018/ijdsst.286693] [Reference Citation Analysis]
7 Xiong L, Sun T, Green R. . MFC 2022;5:93. [DOI: 10.3934/mfc.2021035] [Reference Citation Analysis]
8 Qiu Y, Ding S, Yao N, Gu D, Li X. HFS‐LightGBM : A machine learning model based on hybrid feature selection for classifying ICU patient readmissions. Expert Systems 2021;38. [DOI: 10.1111/exsy.12658] [Reference Citation Analysis]
9 Liu S, Zhang R, Shang X, Li W. Analysis for warning factors of type 2 diabetes mellitus complications with Markov blanket based on a Bayesian network model. Comput Methods Programs Biomed 2020;188:105302. [PMID: 31923820 DOI: 10.1016/j.cmpb.2019.105302] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 3.5] [Reference Citation Analysis]
10 Cui S, Wang Y, Wang D, Sai Q, Huang Z, Cheng TCE. A two-layer nested heterogeneous ensemble learning predictive method for COVID-19 mortality. Appl Soft Comput 2021;113:107946. [PMID: 34646110 DOI: 10.1016/j.asoc.2021.107946] [Reference Citation Analysis]
11 Li J, Ding J, Zhi DU, Gu K, Wang H, De Molon RS. Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm. BioMed Research International 2022;2022:1-15. [DOI: 10.1155/2022/1230761] [Reference Citation Analysis]
12 Lu J, Wang L, Bennamoun M, Ward I, An S, Sohel F, Chow BJW, Dwivedi G, Sanfilippo FM. Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data. Sci Rep 2021;11:18314. [PMID: 34526544 DOI: 10.1038/s41598-021-97643-3] [Reference Citation Analysis]
13 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 DOI: 10.1002/brb3.1794] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
14 Feng Y, Wang D, Yin Y, Li Z, Hu Z. An XGBoost-based casualty prediction method for terrorist attacks. Complex Intell Syst 2020;6:721-40. [DOI: 10.1007/s40747-020-00173-0] [Cited by in Crossref: 15] [Cited by in F6Publishing: 8] [Article Influence: 7.5] [Reference Citation Analysis]
15 Du G, Zhang J, Ma F, Zhao M, Lin Y, Li S. Towards graph-based class-imbalance learning for hospital readmission. Expert Systems with Applications 2021;176:114791. [DOI: 10.1016/j.eswa.2021.114791] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 7.0] [Reference Citation Analysis]
16 Sumathi A, Meganathan S. Semi supervised data mining model for the prognosis of pre-diabetic conditions in type 2 Diabetes Mellitus. Bioinformation 2019;15:875-82. [PMID: 32256007 DOI: 10.6026/97320630015875] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
17 Abujaber A, Fadlalla A, Gammoh D, Abdelrahman H, Mollazehi M, El-Menyar A. Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach. Scand J Trauma Resusc Emerg Med 2020;28:44. [PMID: 32460867 DOI: 10.1186/s13049-020-00738-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Yan F, Feng Y. A two-stage stacked-based heterogeneous ensemble learning for cancer survival prediction. Complex Intell Syst . [DOI: 10.1007/s40747-022-00791-w] [Reference Citation Analysis]
19 Cui S, Yin Y, Wang D, Li Z, Wang Y. A stacking-based ensemble learning method for earthquake casualty prediction. Applied Soft Computing 2021;101:107038. [DOI: 10.1016/j.asoc.2020.107038] [Cited by in Crossref: 17] [Cited by in F6Publishing: 4] [Article Influence: 17.0] [Reference Citation Analysis]
20 Zhang Z, Qiu H, Li W, Chen Y. A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction. BMC Med Inform Decis Mak 2020;20:335. [PMID: 33317534 DOI: 10.1186/s12911-020-01358-w] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
21 Wang N, Zhao S, Cui S, Fan W. A hybrid ensemble learning method for the identification of gang-related arson cases. Knowledge-Based Systems 2021;218:106875. [DOI: 10.1016/j.knosys.2021.106875] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
22 Lin H, Kuo Y, Liu M. A health informatics transformation model based on intelligent cloud computing – exemplified by type 2 diabetes mellitus with related cardiovascular diseases. Computer Methods and Programs in Biomedicine 2020;191:105409. [DOI: 10.1016/j.cmpb.2020.105409] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
23 Schwartz JM, Moy AJ, Rossetti SC, Elhadad N, Cato KD. Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review. J Am Med Inform Assoc 2021;28:653-63. [PMID: 33325504 DOI: 10.1093/jamia/ocaa296] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
24 Wang C, Chen X, Du L, Zhan Q, Yang T, Fang Z. Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease. Comput Methods Programs Biomed 2020;188:105267. [PMID: 31841787 DOI: 10.1016/j.cmpb.2019.105267] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
25 Shi B, Chen J, Chen H, Lin W, Yang J, Chen Y, Wu C, Huang Z. Prediction of recurrent spontaneous abortion using evolutionary machine learning with joint self-adaptive sime mould algorithm. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105885] [Reference Citation Analysis]
26 Seba PA, Benifa JVB. A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments. Wireless Pers Commun. [DOI: 10.1007/s11277-022-09759-y] [Reference Citation Analysis]
27 Cui S, Qiu H, Wang S, Wang Y. Two-stage stacking heterogeneous ensemble learning method for gasoline octane number loss prediction. Applied Soft Computing 2021;113:107989. [DOI: 10.1016/j.asoc.2021.107989] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Zhang Y, Cui S, Gao H. Adverse drug reaction detection on social media with deep linguistic features. J Biomed Inform 2020;106:103437. [PMID: 32360987 DOI: 10.1016/j.jbi.2020.103437] [Cited by in Crossref: 8] [Article Influence: 4.0] [Reference Citation Analysis]
29 Cui S, Wang Y, Yin Y, Cheng T, Wang D, Zhai M. A cluster-based intelligence ensemble learning method for classification problems. Information Sciences 2021;560:386-409. [DOI: 10.1016/j.ins.2021.01.061] [Cited by in Crossref: 9] [Cited by in F6Publishing: 3] [Article Influence: 9.0] [Reference Citation Analysis]
30 Ossai CI, Wickramasinghe N. A hybrid approach for risk stratification and predictive modelling of 30-days unplanned readmission of comorbid patients with diabetes. Journal of Diabetes and its Complications 2022. [DOI: 10.1016/j.jdiacomp.2022.108200] [Reference Citation Analysis]
31 Dagogo-george TE, Mojeed HA, Balogun AO, Mabayoje MA, Salihu SA. Tree-based homogeneous ensemble model with feature selection for diabetic retinopathy prediction. Jurnal Teknologi dan Sistem Komputer 2020;8:297-303. [DOI: 10.14710/jtsiskom.2020.13669] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
32 Chen S, Xu K, Yao X, Zhu S, Zhang B, Zhou H, Guo X, Zhao B. Psychophysiological data-driven multi-feature information fusion and recognition of miner fatigue in high-altitude and cold areas. Comput Biol Med 2021;133:104413. [PMID: 33915363 DOI: 10.1016/j.compbiomed.2021.104413] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Gopukumar D, Ghoshal A, Zhao H. A Machine Learning Approach for Predicting Readmission Charges Billed by Hospitals. JMIR Med Inform 2022. [PMID: 35896038 DOI: 10.2196/37578] [Reference Citation Analysis]
34 Ossai CI, Wickramasinghe N. Intelligent therapeutic decision support for 30 days readmission of diabetic patients with different comorbidities. J Biomed Inform 2020;107:103486. [PMID: 32561445 DOI: 10.1016/j.jbi.2020.103486] [Cited by in Crossref: 4] [Article Influence: 2.0] [Reference Citation Analysis]
35 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 Technol 2021;11:929-40. [DOI: 10.1007/s12553-021-00551-9] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Chen S, Xu K, Yao X, Ge J, Li L, Zhu S, Li Z. Information fusion and multi-classifier system for miner fatigue recognition in plateau environments based on electrocardiography and electromyography signals. Comput Methods Programs Biomed 2021;211:106451. [PMID: 34644668 DOI: 10.1016/j.cmpb.2021.106451] [Reference Citation Analysis]
37 Abujaber A, Fadlalla A, Gammoh D, Abdelrahman H, Mollazehi M, El-Menyar A. Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach. PLoS One 2020;15:e0235231. [PMID: 32639971 DOI: 10.1371/journal.pone.0235231] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Alyami SN, Olatunji SO. Application of Support Vector Machine for Arabic Sentiment Classification Using Twitter-Based Dataset. J Info Know Mgmt 2020;19:2040018. [DOI: 10.1142/s0219649220400183] [Cited by in Crossref: 4] [Article Influence: 2.0] [Reference Citation Analysis]
39 Srivastava AK, Kumar Y, Singh PK. A Rule-Based Monitoring System for Accurate Prediction of Diabetes: Monitoring System for Diabetes. International Journal of E-Health and Medical Communications 2020;11:32-53. [DOI: 10.4018/ijehmc.2020070103] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]