BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Makino M, Yoshimoto R, Ono M, Itoko T, Katsuki T, Koseki A, Kudo M, Haida K, Kuroda J, Yanagiya R, Saitoh E, Hoshinaga K, Yuzawa Y, Suzuki A. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Sci Rep 2019;9:11862. [PMID: 31413285 DOI: 10.1038/s41598-019-48263-5] [Cited by in Crossref: 69] [Cited by in F6Publishing: 74] [Article Influence: 23.0] [Reference Citation Analysis]
Number Citing Articles
1 Kanda E, Suzuki A, Makino M, Tsubota H, Kanemata S, Shirakawa K, Yajima T. Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients. Sci Rep 2022;12:20012. [DOI: 10.1038/s41598-022-24562-2] [Reference Citation Analysis]
2 Kanda E, Okami S, Kohsaka S, Okada M, Ma X, Kimura T, Shirakawa K, Yajima T. Machine Learning Models Predicting Cardiovascular and Renal Outcomes and Mortality in Patients with Hyperkalemia. Nutrients 2022;14:4614. [DOI: 10.3390/nu14214614] [Reference Citation Analysis]
3 Limonte CP, Kretzler M, Pennathur S, Pop-busui R, de Boer IH. Present and future directions in diabetic kidney disease. Journal of Diabetes and its Complications 2022. [DOI: 10.1016/j.jdiacomp.2022.108357] [Reference Citation Analysis]
4 Huang J, Yeung AM, Armstrong DG, Battarbee AN, Cuadros J, Espinoza JC, Kleinberg S, Mathioudakis N, Swerdlow MA, Klonoff DC. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. J Diabetes Sci Technol 2022;:19322968221124583. [PMID: 36121302 DOI: 10.1177/19322968221124583] [Reference Citation Analysis]
5 Wang X, Jiao B, Liu H, Wang Y, Hao X, Zhu Y, Xu B, Xu H, Zhang S, Jia X, Xu Q, Liao X, Zhou Y, Jiang H, Wang J, Guo J, Yan X, Tang B, Zhao R, Shen L. Machine learning based on Optical Coherence Tomography images as a diagnostic tool for Alzheimer's disease. CNS Neurosci Ther. [DOI: 10.1111/cns.13963] [Reference Citation Analysis]
6 Hui D, Sun Y, Xu S, Liu J, He P, Deng Y, Huang H, Zhou X, Li R. Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning. Int Urol Nephrol 2022. [PMID: 36069963 DOI: 10.1007/s11255-022-03322-1] [Reference Citation Analysis]
7 Wang X, Wang Y, Liu H, Zhu X, Hao X, Zhu Y, Xu B, Zhang S, Jia X, Weng L, Liao X, Zhou Y, Tang B, Zhao R, Jiao B, Shen L. Macular Microvascular Density as a Diagnostic Biomarker for Alzheimer’s Disease. JAD 2022. [DOI: 10.3233/jad-220482] [Reference Citation Analysis]
8 Van Vleck TT, Farrell D, Chan L. Natural Language Processing in Nephrology. Adv Chronic Kidney Dis 2022;29:465-71. [PMID: 36253030 DOI: 10.1053/j.ackd.2022.07.001] [Reference Citation Analysis]
9 Baumgartner M, Veeranki S, Hayn D, Schreier G. Introduction and Comparison of Novel Decentral Learning Schemes with Multiple Data Pools for Privacy-preserving ECG Classification.. [DOI: 10.21203/rs.3.rs-1955846/v1] [Reference Citation Analysis]
10 Gulamali FF, Sawant AS, Nadkarni GN. Machine learning for risk stratification in kidney disease. Curr Opin Nephrol Hypertens 2022. [PMID: 36004937 DOI: 10.1097/MNH.0000000000000832] [Reference Citation Analysis]
11 Lee SS, Chang DJ, Kwon JW, Min JW, Jo K, Yoo YS, Lyu B, Baek J. Prediction of Visual Outcomes After Diabetic Vitrectomy Using Clinical Factors From Common Data Warehouse. Transl Vis Sci Technol 2022;11:25. [PMID: 36006638 DOI: 10.1167/tvst.11.8.25] [Reference Citation Analysis]
12 Lim DKE, Boyd JH, Thomas E, Chakera A, Tippaya S, Irish A, Manuel J, Betts K, Robinson S. Prediction models used in the progression of chronic kidney disease: A scoping review. PLoS ONE 2022;17:e0271619. [DOI: 10.1371/journal.pone.0271619] [Reference Citation Analysis]
13 Nicolucci A, Romeo L, Bernardini M, Vespasiani M, Rossi MC, Petrelli M, Ceriello A, Di Bartolo P, Frontoni E, Vespasiani G. Prediction of complications of type 2 Diabetes: A Machine learning approach. Diabetes Res Clin Pract 2022;190:110013. [PMID: 35870573 DOI: 10.1016/j.diabres.2022.110013] [Reference Citation Analysis]
14 Momenzadeh A, Shamsa A, Meyer JG. Bias or biology? Importance of model interpretation in machine learning studies from electronic health records. JAMIA Open 2022;5. [DOI: 10.1093/jamiaopen/ooac063] [Reference Citation Analysis]
15 Zou X, Huang Q, Luo Y, Ren Q, Han X, Zhou X, Ji L. The efficacy of canagliflozin in diabetes subgroups stratified by data-driven clustering or a supervised machine learning method: a post hoc analysis of canagliflozin clinical trial data. Diabetologia 2022. [PMID: 35802168 DOI: 10.1007/s00125-022-05748-9] [Reference Citation Analysis]
16 Zhang W, Liu X, Dong Z, Wang Q, Pei Z, Chen Y, Zheng Y, Wang Y, Chen P, Feng Z, Sun X, Cai G, Chen X. New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients. Front Endocrinol 2022;13:913021. [DOI: 10.3389/fendo.2022.913021] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Gupta S, Garg N, Sinha D, Yadav B, Gupta B, Miah S, Khan R. The Emerging Role of Implementing Machine Learning in Food Recommendation for Chronic Kidney Diseases Using Correlation Analysis. Journal of Food Quality 2022;2022:1-10. [DOI: 10.1155/2022/7176261] [Reference Citation Analysis]
18 Baskozos G, Themistocleous AC, Hebert HL, Pascal MMV, John J, Callaghan BC, Laycock H, Granovsky Y, Crombez G, Yarnitsky D, Rice ASC, Smith BH, Bennett DLH. Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts. BMC Med Inform Decis Mak 2022;22. [DOI: 10.1186/s12911-022-01890-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
19 Loftus TJ, Shickel B, Ozrazgat-Baslanti T, Ren Y, Glicksberg BS, Cao J, Singh K, Chan L, Nadkarni GN, Bihorac A. Artificial intelligence-enabled decision support in nephrology. Nat Rev Nephrol 2022. [PMID: 35459850 DOI: 10.1038/s41581-022-00562-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
20 Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022;11:2265. [PMID: 35456357 DOI: 10.3390/jcm11082265] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 10.0] [Reference Citation Analysis]
21 David SK, Rafiullah M, Siddiqui K, Kumar Reddy MP. Comparison of Different Machine Learning Techniques to Predict Diabetic Kidney Disease. Journal of Healthcare Engineering 2022;2022:1-9. [DOI: 10.1155/2022/7378307] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Sisodia A, Jindal R. An effective model for healthcare to process chronic kidney disease using big data processing. J Ambient Intell Human Comput. [DOI: 10.1007/s12652-022-03817-w] [Reference Citation Analysis]
23 Dong Z, Wang Q, Ke Y, Zhang W, Hong Q, Liu C, Liu X, Yang J, Xi Y, Shi J, Zhang L, Zheng Y, Lv Q, Wang Y, Wu J, Sun X, Cai G, Qiao S, Yin C, Su S, Chen X. Prediction of 3-year risk of diabetic kidney disease using machine learning based on electronic medical records. J Transl Med 2022;20:143. [PMID: 35346252 DOI: 10.1186/s12967-022-03339-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Mushtaq Z, Ramzan MF, Ali S, Baseer S, Samad A, Husnain M, Farouk A. Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques. Mobile Information Systems 2022;2022:1-16. [DOI: 10.1155/2022/6521532] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
25 Momenzadeh A, Shamsa A, Meyer JG. Clinical interpretation of machine learning models for prediction of diabetic complications using electronic health records.. [DOI: 10.1101/2022.03.11.22272039] [Reference Citation Analysis]
26 Schena FP, Magistroni R, Narducci F, Abbrescia DI, Anelli VW, Di Noia T. Artificial intelligence in glomerular diseases. Pediatr Nephrol 2022. [PMID: 35266037 DOI: 10.1007/s00467-021-05419-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
27 Liu YC, Cheng HY, Chang TH, Ho TW, Liu TC, Yen TY, Chou CC, Chang LY, Lai F. Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach. JMIR Med Inform 2022;10:e28934. [PMID: 35084358 DOI: 10.2196/28934] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
28 Allen A, Iqbal Z, Green-Saxena A, Hurtado M, Hoffman J, Mao Q, Das R. Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus. BMJ Open Diabetes Res Care 2022;10:e002560. [PMID: 35046014 DOI: 10.1136/bmjdrc-2021-002560] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 8.0] [Reference Citation Analysis]
29 Seinen TM, Fridgeirsson E, Ioannou S, Jeannetot D, John LH, Kors JA, Markus AF, Pera V, Rekkas A, Williams RD, Yang C, van Mulligen E, Rijnbeek PR. Use of unstructured text in prognostic clinical prediction models: a systematic review.. [DOI: 10.1101/2022.01.17.22269400] [Reference Citation Analysis]
30 Singh V, Asari VK, Rajasekaran R. A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease. Diagnostics 2022;12:116. [DOI: 10.3390/diagnostics12010116] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
31 Vehi J, Mujahid O, Contreras I. Aim and Diabetes. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-64573-1_158] [Reference Citation Analysis]
32 Nakamura K, Mazaki L, Hayashi Y, Tsuji T, Furusawa H. Predicting the Classification of Home Oxygen Therapy for Post-COVID-19 Rehabilitation Patients Using a Neural Network. Phys Ther Res 2022. [DOI: 10.1298/ptr.e10181] [Reference Citation Analysis]
33 Ye Q, Kuroda T, Ruan T, Zhang W, Ge X. An Integrated Resampling Methods for Imbalanced Sporadic Temporal Data in EHRs. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021. [DOI: 10.1109/bibm52615.2021.9669865] [Reference Citation Analysis]
34 Pervaiz U, Bukhari F, Iqbal W. A Study on Detection of Chronic Renal Failure Based on Machine Learning. 2021 International Conference on Innovative Computing (ICIC) 2021. [DOI: 10.1109/icic53490.2021.9693074] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Tan KR, Seng JJB, Kwan YH, Chen YJ, Zainudin SB, Loh DHF, Liu N, Low LL. Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review. J Diabetes Sci Technol 2021;:19322968211056917. [PMID: 34727783 DOI: 10.1177/19322968211056917] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Lin Y, Khong PL, Zou Z, Cao P. Evaluation of pediatric hydronephrosis using deep learning quantification of fluid-to-kidney-area ratio by ultrasonography. Abdom Radiol (NY) 2021;46:5229-39. [PMID: 34227014 DOI: 10.1007/s00261-021-03201-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
37 Abrar SM. Artificial Intelligence for the Diagnosis and Treatment of Diabetes Kidney Disease: a systematic review.. [DOI: 10.1101/2021.10.10.21264813] [Reference Citation Analysis]
38 Salna I, Salna E, Pahirko L, Skrebinska S, Krikova R, Folkmane I, Pīrāgs V, Sokolovska J. Achievement of treatment targets predicts progression of vascular complications in type 1 diabetes. J Diabetes Complications 2021;35:108072. [PMID: 34635403 DOI: 10.1016/j.jdiacomp.2021.108072] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 Kulzer B. Wie profitieren Menschen mit Diabetes von Big Data und künstlicher Intelligenz? Diabetologe 2021;17:799-806. [DOI: 10.1007/s11428-021-00818-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
40 Oba Y, Tezuka T, Sanuki M, Wagatsuma Y. Interpretable Prediction of Diabetes from Tabular Health Screening Records Using an Attentional Neural Network. 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) 2021. [DOI: 10.1109/dsaa53316.2021.9564151] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
41 Liu Y, Jin P. Analysis of the Status Quo of College English Learning Based On Big Data Technology. 2021 4th International Conference on Information Systems and Computer Aided Education 2021. [DOI: 10.1145/3482632.3483006] [Reference Citation Analysis]
42 Ventrella P, Delgrossi G, Ferrario G, Righetti M, Masseroli M. Supervised machine learning for the assessment of Chronic Kidney Disease advancement. Comput Methods Programs Biomed 2021;209:106329. [PMID: 34418814 DOI: 10.1016/j.cmpb.2021.106329] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
43 Du A, Shi X, Guo X, Pei Q, Ding Y, Zhou W, Lu Q, Shi H. Assessing the Adequacy of Hemodialysis Patients via the Graph-Based Takagi-Sugeno-Kang Fuzzy System. Comput Math Methods Med 2021;2021:9036322. [PMID: 34367320 DOI: 10.1155/2021/9036322] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
44 Klimontov VV, Berikov VB, Saik OV. Artificial intelligence in diabetology. Diabetes mellitus 2021;24:156-166. [DOI: 10.14341/dm12665] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
45 Kohsaka S, Morita N, Okami S, Kidani Y, Yajima T. Current trends in diabetes mellitus database research in Japan. Diabetes Obes Metab 2021;23 Suppl 2:3-18. [PMID: 33835639 DOI: 10.1111/dom.14325] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
46 Kaur N, Bhattacharya S, Butte AJ. Big Data in Nephrology. Nat Rev Nephrol 2021. [PMID: 34194006 DOI: 10.1038/s41581-021-00439-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
47 Wang L, Xie H, Han W, Yang X, Shi L, Dong J, Jiang K, Wu H. Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences. Comput Assist Surg (Abingdon) 2020;25:29-35. [PMID: 33275462 DOI: 10.1080/24699322.2020.1850866] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
48 Zaccardi F, Davies MJ, Khunti K. The present and future scope of real-world evidence research in diabetes: What questions can and cannot be answered and what might be possible in the future? Diabetes Obes Metab 2020;22 Suppl 3:21-34. [PMID: 32250528 DOI: 10.1111/dom.13929] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 9.0] [Reference Citation Analysis]
49 Zhou L, Zheng X, Yang D, Wang Y, Bai X, Ye X. Application of multi-label classification models for the diagnosis of diabetic complications. BMC Med Inform Decis Mak 2021;21:182. [PMID: 34098959 DOI: 10.1186/s12911-021-01525-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
50 Wei Y, Jiang Z. The evolution and future of diabetic kidney disease research: a bibliometric analysis. BMC Nephrol 2021;22:158. [PMID: 33926393 DOI: 10.1186/s12882-021-02369-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
51 Najafi B, Mishra R. Harnessing Digital Health Technologies to Remotely Manage Diabetic Foot Syndrome: A Narrative Review. Medicina (Kaunas) 2021;57:377. [PMID: 33919683 DOI: 10.3390/medicina57040377] [Cited by in Crossref: 11] [Cited by in F6Publishing: 14] [Article Influence: 11.0] [Reference Citation Analysis]
52 Liu Y, Cheng H, Chang T, Ho T, Liu T, Yen T, Chou C, Chang L, Lai F. Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach (Preprint).. [DOI: 10.2196/preprints.28934] [Reference Citation Analysis]
53 Caly H, Rabiei H, Coste-Mazeau P, Hantz S, Alain S, Eyraud JL, Chianea T, Caly C, Makowski D, Hadjikhani N, Lemonnier E, Ben-Ari Y. Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD. Sci Rep 2021;11:6877. [PMID: 33767300 DOI: 10.1038/s41598-021-86320-0] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 12.0] [Reference Citation Analysis]
54 Turchin A, Florez Builes LF. Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review. J Diabetes Sci Technol 2021;15:553-60. [PMID: 33736486 DOI: 10.1177/19322968211000831] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
55 Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Physica Medica 2021;83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 13.0] [Reference Citation Analysis]
56 Ravaut M, Sadeghi H, Leung KK, Volkovs M, Kornas K, Harish V, Watson T, Lewis GF, Weisman A, Poutanen T, Rosella L. Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data. NPJ Digit Med 2021;4:24. [PMID: 33580109 DOI: 10.1038/s41746-021-00394-8] [Cited by in Crossref: 18] [Cited by in F6Publishing: 18] [Article Influence: 18.0] [Reference Citation Analysis]
57 Palmer MB, Abedini A, Jackson C, Blady S, Chatterjee S, Sullivan KM, Townsend RR, Brodbeck J, Almaani S, Srivastava A, Avasare R, Ross MJ, Mottl AK, Argyropoulos C, Hogan J, Susztak K. The Role of Glomerular Epithelial Injury in Kidney Function Decline in Patients With Diabetic Kidney Disease in the TRIDENT Cohort. Kidney Int Rep 2021;6:1066-80. [PMID: 33912757 DOI: 10.1016/j.ekir.2021.01.025] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
58 Raeesi Vanani I, Amirhosseini M. IoT-Based Diseases Prediction and Diagnosis System for Healthcare. Studies in Big Data 2021. [DOI: 10.1007/978-981-15-4112-4_2] [Cited by in Crossref: 6] [Article Influence: 6.0] [Reference Citation Analysis]
59 Vehi J, Mujahid O, Contreras I. Aim and Diabetes. Artificial Intelligence in Medicine 2021. [DOI: 10.1007/978-3-030-58080-3_158-1] [Reference Citation Analysis]
60 Battula VK, Satheesh P, Srinivas B, Chandra Sekhar A, Aswini Sujatha V. Role of Advanced Glycated End Products (AGEs) in Predicting Diabetic Complications Using Machine Learning Tools: A Review from Biological Perspective. Lecture Notes in Electrical Engineering 2021. [DOI: 10.1007/978-981-15-7961-5_138] [Reference Citation Analysis]
61 Yao L, Zhang H, Zhang M, Chen X, Zhang J, Huang J, Zhang L. Application of artificial intelligence in renal disease. Clinical eHealth 2021;4:54-61. [DOI: 10.1016/j.ceh.2021.11.003] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
62 Romiyo P, Ding K, Dejam D, Franks A, Ng E, Preet K, Tucker AM, Niu T, Nagasawa DT, Rahman S, Yang I. Systematic review and evaluation of predictive modeling algorithms in spinal surgeries. J Neurol Sci 2021;420:117184. [PMID: 33203588 DOI: 10.1016/j.jns.2020.117184] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
63 Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol 2020;56:498-505. [PMID: 32530098 DOI: 10.1002/uog.22122] [Cited by in Crossref: 52] [Cited by in F6Publishing: 52] [Article Influence: 26.0] [Reference Citation Analysis]
64 Matsuoka R, Akazawa H, Kodera S, Komuro I. The dawning of the digital era in the management of hypertension. Hypertens Res 2020;43:1135-40. [PMID: 32655134 DOI: 10.1038/s41440-020-0506-1] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 4.5] [Reference Citation Analysis]
65 Caly H, Rabiei H, Coste-mazeau P, Hantz S, Alain S, Eyraud J, Chianea T, Caly C, Makowski D, Hadjikhani N, Lemonnier E, Ben-ari Y. Pregnancy data enable identification of relevant biomarkers and a partial prognosis of autism at birth.. [DOI: 10.1101/2020.07.08.192989] [Reference Citation Analysis]
66 Emile SH, Hamid HKS. Fighting COVID-19, a place for artificial intelligence. Transbound Emerg Dis 2020. [PMID: 32460383 DOI: 10.1111/tbed.13648] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
67 Hueso M, de Haro L, Calabia J, Dal-Ré R, Tebé C, Gibert K, Cruzado JM, Vellido A. Leveraging Data Science for a Personalized Haemodialysis. Kidney Dis (Basel) 2020;6:385-94. [PMID: 33313059 DOI: 10.1159/000507291] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
68 Chen R. Machine learning for ovarian cancer: lasso regression-based predictive model of early mortality in patients with stage I and stage II ovarian cancer.. [DOI: 10.1101/2020.05.01.20088294] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
69 Chen R, Kudelka MR, Rosado AM, Zhang J. Machine learning algorithm for early mortality prediction in patients with advanced penile cancer.. [DOI: 10.1101/2020.04.22.20074955] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
70 Yuan Q, Zhang H, Deng T, Tang S, Yuan X, Tang W, Xie Y, Ge H, Wang X, Zhou Q, Xiao X. Role of Artificial Intelligence in Kidney Disease. Int J Med Sci 2020;17:970-84. [PMID: 32308551 DOI: 10.7150/ijms.42078] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 6.5] [Reference Citation Analysis]
71 Muse ED, Topol EJ. A brighter future for kidney disease? Lancet 2020;395:179. [PMID: 31954451 DOI: 10.1016/S0140-6736(20)30061-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]