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For: Jiang T, Gradus JL, Rosellini AJ. Supervised Machine Learning: A Brief Primer. Behav Ther 2020;51:675-87. [PMID: 32800297 DOI: 10.1016/j.beth.2020.05.002] [Cited by in Crossref: 15] [Cited by in F6Publishing: 8] [Article Influence: 7.5] [Reference Citation Analysis]
Number Citing Articles
1 Tetik B, Mert Doğan G, Paşahan R, Durak MA, Güldoğan E, Saraç K, Önal Ç, Yıldırım İO. Multi-parameter-based radiological diagnosis of Chiari Malformation using Machine Learning Technology. Int J Clin Pract 2021;75:e14746. [PMID: 34428317 DOI: 10.1111/ijcp.14746] [Reference Citation Analysis]
2 Liu D, Feng XL, Ahmed F, Shahid M, Guo J. Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review. JMIR Ment Health 2022;9:e27244. [PMID: 35230252 DOI: 10.2196/27244] [Reference Citation Analysis]
3 Samadi Gharajeh M, Jond HB. Speed Control for Leader-Follower Robot Formation Using Fuzzy System and Supervised Machine Learning. Sensors (Basel) 2021;21:3433. [PMID: 34069186 DOI: 10.3390/s21103433] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
4 Zelkowitz RL, Jiang T, Horváth-Puhó E, Street AE, Lash TL, Sørensen HT, Rosellini AJ, Gradus JL. Predictors of nonfatal suicide attempts within 30 days of discharge from psychiatric hospitalization: Sex-specific models developed using population-based registries. J Affect Disord 2022:S0165-0327(22)00274-9. [PMID: 35304235 DOI: 10.1016/j.jad.2022.03.034] [Reference Citation Analysis]
5 Ameli N, Gibson MP, Khanna A, Howey M, Lai H. An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes. Front Dent Med 2022;3:833191. [DOI: 10.3389/fdmed.2022.833191] [Reference Citation Analysis]
6 Liu K, Droncheff B, Warren SL. Predictive Utility of Symptom Measures in Classifying Anxiety and Depression: A Machine-Learning Approach. Psychiatry Research 2022. [DOI: 10.1016/j.psychres.2022.114534] [Reference Citation Analysis]
7 Dang Q. Improving the performance of the intrusion detection systems by the machine learning explainability. IJWIS 2021;17:537-55. [DOI: 10.1108/ijwis-03-2021-0022] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
8 Jin J, Zhou H, Sun S, Tian Z, Ren H, Feng J. Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis. Cancer Manag Res 2021;13:8967-77. [PMID: 34880677 DOI: 10.2147/CMAR.S337516] [Reference Citation Analysis]
9 Houssein EH, Abohashima Z, Elhoseny M, Mohamed WM. Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision. Expert Systems with Applications 2022;194:116512. [DOI: 10.1016/j.eswa.2022.116512] [Reference Citation Analysis]
10 Dyer AS, Zaengle D, Nelson JR, Duran R, Wenzlick M, Wingo PC, Bauer JR, Rose K, Romeo L. Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks. Marine Structures 2022;83:103152. [DOI: 10.1016/j.marstruc.2021.103152] [Reference Citation Analysis]
11 Ćosić K, Popović S, Šarlija M, Kesedžić I, Gambiraža M, Dropuljić B, Mijić I, Henigsberg N, Jovanovic T. AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients. Front Psychol 2021;12:782866. [PMID: 35027902 DOI: 10.3389/fpsyg.2021.782866] [Reference Citation Analysis]
12 Alanazi A. Using machine learning for healthcare challenges and opportunities. Informatics in Medicine Unlocked 2022;30:100924. [DOI: 10.1016/j.imu.2022.100924] [Reference Citation Analysis]
13 Ishaque S, Khan N, Krishnan S. Comprehending the impact of deep learning algorithms on optimizing for recurring impediments associated with stress prediction using ECG data through statistical analysis. Biomedical Signal Processing and Control 2022;74:103484. [DOI: 10.1016/j.bspc.2022.103484] [Reference Citation Analysis]
14 Khan SR, Al Rijjal D, Piro A, Wheeler MB. Integration of AI and traditional medicine in drug discovery. Drug Discov Today 2021;26:982-92. [PMID: 33476566 DOI: 10.1016/j.drudis.2021.01.008] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 Xu P, Chen H, Li M, Lu W. New Opportunity: Machine Learning for Polymer Materials Design and Discovery. Advcd Theory and Sims. [DOI: 10.1002/adts.202100565] [Reference Citation Analysis]
16 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]
17 Urtubia A, León R, Vargas M. Identification of chemical markers to detect abnormal wine fermentation using support vector machines. Computers & Chemical Engineering 2021;145:107158. [DOI: 10.1016/j.compchemeng.2020.107158] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
18 Adams RS, Jiang T, Rosellini AJ, Horváth-Puhó E, Street AE, Keyes KM, Cerdá M, Lash TL, Sørensen HT, Gradus JL. Sex-Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark. Addiction 2021;116:2882-92. [PMID: 33620758 DOI: 10.1111/add.15455] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 Yland JJ, Wang T, Zad Z, Willis SK, Wang TR, Wesselink AK, Jiang T, Hatch EE, Wise LA, Paschalidis IC. Predictive models of pregnancy based on data from a preconception cohort study. Hum Reprod 2022:deab280. [PMID: 35024824 DOI: 10.1093/humrep/deab280] [Reference Citation Analysis]
20 Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv 2021;49:107739. [PMID: 33794304 DOI: 10.1016/j.biotechadv.2021.107739] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
21 Kotlarz K, Mielczarek M, Suchocki T, Czech B, Guldbrandtsen B, Szyda J. The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines. J Appl Genet 2020;61:607-16. [PMID: 32996082 DOI: 10.1007/s13353-020-00586-0] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
22 Brdesee HS, Alsaggaf W, Aljohani N, Hassan S. Predictive Model Using a Machine Learning Approach for Enhancing the Retention Rate of Students At-Risk: . International Journal on Semantic Web and Information Systems 2022;18:1-21. [DOI: 10.4018/ijswis.299859] [Reference Citation Analysis]
23 Gradus JL, Rosellini AJ, Szentkúti P, Horváth-Puhó E, Smith ML, Galatzer-Levy I, Lash TL, Galea S, Schnurr PP, Sørensen HT. Pre-trauma predictors of severe psychiatric comorbidity 5 years following traumatic experiences. Int J Epidemiol 2022:dyac030. [PMID: 35179599 DOI: 10.1093/ije/dyac030] [Reference Citation Analysis]
24 Zmigrod L, Eisenberg IW, Bissett PG, Robbins TW, Poldrack RA. The cognitive and perceptual correlates of ideological attitudes: a data-driven approach. Philos Trans R Soc Lond B Biol Sci 2021;376:20200424. [PMID: 33611995 DOI: 10.1098/rstb.2020.0424] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 8.0] [Reference Citation Analysis]
25 Abd-Algaleel SA, Abdel-Bar HM, Metwally AA, Hathout RM. Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers. Pharmaceuticals (Basel) 2021;14:645. [PMID: 34358071 DOI: 10.3390/ph14070645] [Reference Citation Analysis]
26 Al Turkestani N, Bianchi J, Deleat-Besson R, Le C, Tengfei L, Prieto JC, Gurgel M, Ruellas ACO, Massaro C, Aliaga Del Castillo A, Evangelista K, Yatabe M, Benavides E, Soki F, Zhang W, Najarian K, Gryak J, Styner M, Fillion-Robin JC, Paniagua B, Soroushmehr R, Cevidanes LHS. Clinical decision support systems in orthodontics: A narrative review of data science approaches. Orthod Craniofac Res 2021. [PMID: 33973362 DOI: 10.1111/ocr.12492] [Cited by in F6Publishing: 1] [Reference Citation Analysis]