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For: Richter AN, Khoshgoftaar TM. A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artificial Intelligence in Medicine 2018;90:1-14. [DOI: 10.1016/j.artmed.2018.06.002] [Cited by in Crossref: 64] [Cited by in F6Publishing: 68] [Article Influence: 16.0] [Reference Citation Analysis]
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13 Laios A, De Oliveira Silva RV, Dantas De Freitas DL, Tan YS, Saalmink G, Zubayraeva A, Johnson R, Kaufmann A, Otify M, Hutson R, Thangavelu A, Broadhead T, Nugent D, Theophilou G, Gomes de Lima KM, De Jong D. Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score. J Clin Med 2021;11:87. [PMID: 35011828 DOI: 10.3390/jcm11010087] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Rawat S, Rawat A, Kumar D, Sabitha AS. Application of machine learning and data visualization techniques for decision support in the insurance sector. International Journal of Information Management Data Insights 2021;1:100012. [DOI: 10.1016/j.jjimei.2021.100012] [Cited by in Crossref: 22] [Cited by in F6Publishing: 9] [Article Influence: 22.0] [Reference Citation Analysis]
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17 Abdullah Alfayez A, Kunz H, Grace Lai A. Predicting the risk of cancer in adults using supervised machine learning: a scoping review. BMJ Open 2021;11:e047755. [PMID: 34521662 DOI: 10.1136/bmjopen-2020-047755] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
18 Yunusov V, Demin S, Rusanova I, Demina N. The study of the self-similar nature of human neuromagnetic responses when exposed to flickering light stimuli. 2021 International Conference "Nonlinearity, Information and Robotics" (NIR) 2021. [DOI: 10.1109/nir52917.2021.9666068] [Reference Citation Analysis]
19 Oei RW, Lyu Y, Ye L, Kong F, Du C, Zhai R, Xu T, Shen C, He X, Kong L, Hu C, Ying H. Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics. J Pers Med 2021;11:787. [PMID: 34442430 DOI: 10.3390/jpm11080787] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
20 Nordin N, Zainol Z, Mohd Noor MH, Lai Fong C. A comparative study of machine learning techniques for suicide attempts predictive model. Health Informatics J 2021;27:1460458221989395. [PMID: 33745355 DOI: 10.1177/1460458221989395] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
21 Lui TK, Cheung KSM, Leung WK. One- year mortality in patients with advanced hepatocellular carcinoma on immunotherapy: Prediction using machine learning models (Preprint).. [DOI: 10.2196/preprints.32281] [Reference Citation Analysis]
22 Chakraborty D, Ivan C, Amero P, Khan M, Rodriguez-Aguayo C, Başağaoğlu H, Lopez-Berestein G. Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer. Cancers (Basel) 2021;13:3450. [PMID: 34298668 DOI: 10.3390/cancers13143450] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
23 Francisco ME, Carvajal TM, Ryo M, Nukazawa K, Amalin DM, Watanabe K. Dengue disease dynamics are modulated by the combined influences of precipitation and landscape: A machine learning approach. Sci Total Environ 2021;792:148406. [PMID: 34157535 DOI: 10.1016/j.scitotenv.2021.148406] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
24 Cortés-Ibañez FO, Belur Nagaraj S, Cornelissen L, Sidorenkov G, de Bock GH. A Classification Approach for Cancer Survivors from Those Cancer-Free, Based on Health Behaviors: Analysis of the Lifelines Cohort. Cancers (Basel) 2021;13:2335. [PMID: 34066093 DOI: 10.3390/cancers13102335] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Greenberg JK, Otun A, Ghogawala Z, Yen PY, Molina CA, Limbrick DD Jr, Foraker RE, Kelly MP, Ray WZ. Translating Data Analytics Into Improved Spine Surgery Outcomes: A Roadmap for Biomedical Informatics Research in 2021. Global Spine J 2021;:21925682211008424. [PMID: 33973491 DOI: 10.1177/21925682211008424] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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28 Li J, Zhou Z, Dong J, Fu Y, Li Y, Luan Z, Peng X. Predicting breast cancer 5-year survival using machine learning: A systematic review. PLoS One 2021;16:e0250370. [PMID: 33861809 DOI: 10.1371/journal.pone.0250370] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 19.0] [Reference Citation Analysis]
29 Hwangbo S, Kim SI, Kim JH, Eoh KJ, Lee C, Kim YT, Suh DS, Park T, Song YS. Development of Machine Learning Models to Predict Platinum Sensitivity of High-Grade Serous Ovarian Carcinoma. Cancers (Basel) 2021;13:1875. [PMID: 33919797 DOI: 10.3390/cancers13081875] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
30 Murthy NS, Bethala C. Review paper on research direction towards cancer prediction and prognosis using machine learning and deep learning models. J Ambient Intell Human Comput. [DOI: 10.1007/s12652-021-03147-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
31 Yang DX, Khera R, Miccio JA, Jairam V, Chang E, Yu JB, Park HS, Krumholz HM, Aneja S. Prevalence of Missing Data in the National Cancer Database and Association With Overall Survival. JAMA Netw Open 2021;4:e211793. [PMID: 33755165 DOI: 10.1001/jamanetworkopen.2021.1793] [Cited by in Crossref: 15] [Cited by in F6Publishing: 20] [Article Influence: 15.0] [Reference Citation Analysis]
32 Cirillo D, Núñez-Carpintero I, Valencia A. Artificial intelligence in cancer research: learning at different levels of data granularity. Mol Oncol 2021;15:817-29. [PMID: 33533192 DOI: 10.1002/1878-0261.12920] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
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35 Shorten C, Khoshgoftaar TM, Furht B. Deep Learning applications for COVID-19. J Big Data 2021;8:18. [PMID: 33457181 DOI: 10.1186/s40537-020-00392-9] [Cited by in Crossref: 79] [Cited by in F6Publishing: 89] [Article Influence: 79.0] [Reference Citation Analysis]
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37 Chu J, Dong W, Wang J, He K, Huang Z. Treatment effect prediction with adversarial deep learning using electronic health records. BMC Med Inform Decis Mak 2020;20:139. [PMID: 33317502 DOI: 10.1186/s12911-020-01151-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
38 Nisha Jenipher V, Radhika S. A Study on Early Prediction of Lung Cancer Using Machine Learning Techniques. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) 2020. [DOI: 10.1109/iciss49785.2020.9316064] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
39 Yang DX, Khera R, Miccio JA, Jairam V, Chang E, Yu JB, Park HS, Krumholz HM, Aneja S. Prevalence of missing data in the National Cancer Database and association with overall survival.. [DOI: 10.1101/2020.10.30.20220855] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
40 Kaieski N, da Costa CA, da Rosa Righi R, Lora PS, Eskofier B. Application of artificial intelligence methods in vital signs analysis of hospitalized patients: A systematic literature review. Applied Soft Computing 2020;96:106612. [DOI: 10.1016/j.asoc.2020.106612] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
41 Bohannan Z, Pudupakam RS, Koo J, Horwitz H, Tsang J, Polley A, Han EJ, Fernandez E, Park S, Swartzfager D, Qi NSX, Tu C, Rankin WV, Thamm DH, Lee HR, Lim S. Predicting likelihood of in vivo chemotherapy response in canine lymphoma using ex vivo drug sensitivity and immunophenotyping data in a machine learning model. Vet Comp Oncol 2021;19:160-71. [PMID: 33025640 DOI: 10.1111/vco.12656] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
42 Francisco ME, Carvajal TM, Ryo M, Nukazawa K, Amalin DM, Watanabe K. Dengue Disease Dynamics are Modulated by the Combined Influence of Precipitation and Landscapes: A Machine Learning-based Approach.. [DOI: 10.1101/2020.09.01.278713] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
43 Almangush A, Mäkitie AA, Triantafyllou A, de Bree R, Strojan P, Rinaldo A, Hernandez-Prera JC, Suárez C, Kowalski LP, Ferlito A, Leivo I. Staging and grading of oral squamous cell carcinoma: An update. Oral Oncol 2020;107:104799. [PMID: 32446214 DOI: 10.1016/j.oraloncology.2020.104799] [Cited by in Crossref: 69] [Cited by in F6Publishing: 63] [Article Influence: 34.5] [Reference Citation Analysis]
44 Bernardini M, Morettini M, Romeo L, Frontoni E, Burattini L. Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach. Artif Intell Med 2020;105:101847. [PMID: 32505428 DOI: 10.1016/j.artmed.2020.101847] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 8.0] [Reference Citation Analysis]
45 Wang J, Yang B, Li Z, Qu J, Liu J, Song N, Chen Y, Cheng Y, Zhang S, Wang Z, Qu X, Liu Y. Nomogram-based prediction of survival in unresectable or metastatic gastric cancer patients with good performance status who received first-line chemotherapy. Ann Transl Med 2020;8:311. [PMID: 32355755 DOI: 10.21037/atm.2020.02.131] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
46 Wang H, Tan X, Huang Z, Pan B, Tian J. Mining incomplete clinical data for the early assessment of Kawasaki disease based on feature clustering and convolutional neural networks. Artificial Intelligence in Medicine 2020;105:101859. [DOI: 10.1016/j.artmed.2020.101859] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
47 Vianna LS, Wazlawick RS. Data Mining for Hospital Morbidity Forecasting. 2020 IEEE International Conference on Software Architecture Companion (ICSA-C) 2020. [DOI: 10.1109/icsa-c50368.2020.00037] [Reference Citation Analysis]
48 Tang X, Gu X, Wang J, He Q, Zhang F, Lu J. A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion. IEEE Access 2020;8:20523-36. [DOI: 10.1109/access.2020.2968519] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 8.0] [Reference Citation Analysis]
49 Hui H, Zhi Y, Xi N, Liu Y. A Weighted Voting Framework for Android App’s Vetting Based on Multiple Machine Learning Models. Network and System Security 2020. [DOI: 10.1007/978-3-030-65745-1_4] [Reference Citation Analysis]
50 Benítez-mata B, Castro C, Castañeda R, Vargas E, Flores D. Prediction of Breast Cancer Diagnosis by Blood Biomarkers Using Artificial Neural Networks. IFMBE Proceedings 2020. [DOI: 10.1007/978-3-030-30648-9_7] [Reference Citation Analysis]
51 Courtenay LA, Huguet R, González-aguilera D, Yravedra J. A Hybrid Geometric Morphometric Deep Learning Approach for Cut and Trampling Mark Classification. Applied Sciences 2020;10:150. [DOI: 10.3390/app10010150] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 3.7] [Reference Citation Analysis]
52 Cesselli D, Ius T, Isola M, Del Ben F, Da Col G, Bulfoni M, Turetta M, Pegolo E, Marzinotto S, Scott CA, Mariuzzi L, Di Loreto C, Beltrami AP, Skrap M. Application of an Artificial Intelligence Algorithm to Prognostically Stratify Grade II Gliomas. Cancers (Basel) 2019;12:E50. [PMID: 31877896 DOI: 10.3390/cancers12010050] [Cited by in Crossref: 13] [Cited by in F6Publishing: 14] [Article Influence: 4.3] [Reference Citation Analysis]
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54 Richter AN, Khoshgoftaar TM. Melanoma risk modeling from limited positive samples. Netw Model Anal Health Inform Bioinforma 2019;8:7. [DOI: 10.1007/s13721-019-0186-4] [Cited by in Crossref: 3] [Article Influence: 1.0] [Reference Citation Analysis]
55 Liu T, Fan W, Wu C. A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset. Artif Intell Med 2019;101:101723. [PMID: 31813482 DOI: 10.1016/j.artmed.2019.101723] [Cited by in Crossref: 41] [Cited by in F6Publishing: 21] [Article Influence: 13.7] [Reference Citation Analysis]
56 Taylor CO, Tarczy-Hornoch P. Personalized Medicine Implementation with Non-traditional Data Sources: A Conceptual Framework and Survey of the Literature. Yearb Med Inform 2019;28:181-9. [PMID: 31419830 DOI: 10.1055/s-0039-1677916] [Reference Citation Analysis]
57 Cueto-López N, García-Ordás MT, Dávila-Batista V, Moreno V, Aragonés N, Alaiz-Rodríguez R. A comparative study on feature selection for a risk prediction model for colorectal cancer. Comput Methods Programs Biomed 2019;177:219-29. [PMID: 31319951 DOI: 10.1016/j.cmpb.2019.06.001] [Cited by in Crossref: 22] [Cited by in F6Publishing: 24] [Article Influence: 7.3] [Reference Citation Analysis]
58 Richter AN, Khoshgoftaar TM. Efficient learning from big data for cancer risk modeling: A case study with melanoma. Comput Biol Med 2019;110:29-39. [PMID: 31112896 DOI: 10.1016/j.compbiomed.2019.04.039] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 6.0] [Reference Citation Analysis]
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64 Gu X, Zhang C, Ni T. Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification. IEEE Access 2019;7:169029-37. [DOI: 10.1109/access.2019.2954707] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
65 Richter AN, Khoshgoftaar TM. Building and Interpreting Risk Models from Imbalanced Clinical Data. 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) 2018. [DOI: 10.1109/ictai.2018.00031] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]