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For: Escandell-Montero P, Chermisi M, Martínez-Martínez JM, Gómez-Sanchis J, Barbieri C, Soria-Olivas E, Mari F, Vila-Francés J, Stopper A, Gatti E, Martín-Guerrero JD. Optimization of anemia treatment in hemodialysis patients via reinforcement learning. Artif Intell Med 2014;62:47-60. [PMID: 25091172 DOI: 10.1016/j.artmed.2014.07.004] [Cited by in Crossref: 29] [Cited by in F6Publishing: 25] [Article Influence: 3.6] [Reference Citation Analysis]
Number Citing Articles
1 Escandell-montero P, Lorente D, Martínez-martínez JM, Soria-olivas E, Vila-francés J, Martín-guerrero JD. Online fitted policy iteration based on extreme learning machines. Knowledge-Based Systems 2016;100:200-11. [DOI: 10.1016/j.knosys.2016.03.007] [Cited by in Crossref: 9] [Article Influence: 1.5] [Reference Citation Analysis]
2 Brier ME, Gaweda AE, Aronoff GR. Personalized Anemia Management and Precision Medicine in ESA and Iron Pharmacology in End-Stage Kidney Disease. Semin Nephrol 2018;38:410-7. [PMID: 30082060 DOI: 10.1016/j.semnephrol.2018.05.010] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 3.3] [Reference Citation Analysis]
3 Maier C, Hartung N, Kloft C, Huisinga W, de Wiljes J. Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology. CPT Pharmacometrics Syst Pharmacol 2021;10:241-54. [PMID: 33470053 DOI: 10.1002/psp4.12588] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
4 Barbieri C, Molina M, Ponce P, Tothova M, Cattinelli I, Ion Titapiccolo J, Mari F, Amato C, Leipold F, Wehmeyer W, Stuard S, Stopper A, Canaud B. An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients. Kidney Int 2016;90:422-9. [PMID: 27262365 DOI: 10.1016/j.kint.2016.03.036] [Cited by in Crossref: 44] [Cited by in F6Publishing: 46] [Article Influence: 7.3] [Reference Citation Analysis]
5 Yu C, Liu J, Zhao H. Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units. BMC Med Inform Decis Mak 2019;19:57. [PMID: 30961594 DOI: 10.1186/s12911-019-0763-6] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 3.7] [Reference Citation Analysis]
6 Yun HR, Lee G, Jeon MJ, Kim HW, Joo YS, Kim H, Chang TI, Park JT, Han SH, Kang SW, Kim W, Yoo TH. Erythropoiesis stimulating agent recommendation model using recurrent neural networks for patient with kidney failure with replacement therapy. Comput Biol Med 2021;137:104718. [PMID: 34481182 DOI: 10.1016/j.compbiomed.2021.104718] [Reference Citation Analysis]
7 Burlacu A, Iftene A, Jugrin D, Popa IV, Lupu PM, Vlad C, Covic A. Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review. Biomed Res Int 2020;2020:9867872. [PMID: 32596403 DOI: 10.1155/2020/9867872] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
8 Ohara T, Ikeda H, Sugitani Y, Suito H, Huynh VQH, Kinomura M, Haraguchi S, Sakurama K. Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients. Int J Med Sci 2021;18:1831-9. [PMID: 33746600 DOI: 10.7150/ijms.53298] [Reference Citation Analysis]
9 McComb M, Bies R, Ramanathan M. Machine learning in pharmacometrics: Opportunities and challenges. Br J Clin Pharmacol 2021. [PMID: 33634893 DOI: 10.1111/bcp.14801] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
10 Zheng H, Zhu J, Xie W, Zhong J. Reinforcement learning assisted oxygen therapy for COVID-19 patients under intensive care. BMC Med Inform Decis Mak 2021;21:350. [PMID: 34920724 DOI: 10.1186/s12911-021-01712-6] [Reference Citation Analysis]
11 Bi Z, Wang M, Ni L, Ye G, Zhou D, Yan C, Zeng X, Chen J. A Practical Electronic Health Record-Based Dry Weight Supervision Model for Hemodialysis Patients. IEEE J Transl Eng Health Med 2019;7:4200109. [PMID: 32309061 DOI: 10.1109/JTEHM.2019.2948604] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
12 Pellicer-Valero OJ, Cattinelli I, Neri L, Mari F, Martín-Guerrero JD, Barbieri C. Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks. Artif Intell Med 2020;107:101898. [PMID: 32828446 DOI: 10.1016/j.artmed.2020.101898] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
13 Thongprayoon C, Kaewput W, Kovvuru K, Hansrivijit P, Kanduri SR, Bathini T, Chewcharat A, Leeaphorn N, Gonzalez-Suarez ML, Cheungpasitporn W. Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation. J Clin Med 2020;9:E1107. [PMID: 32294906 DOI: 10.3390/jcm9041107] [Cited by in Crossref: 18] [Cited by in F6Publishing: 12] [Article Influence: 9.0] [Reference Citation Analysis]
14 Perna G, Grassi M, Caldirola D, Nemeroff CB. The revolution of personalized psychiatry: will technology make it happen sooner? Psychol Med 2018;48:705-13. [DOI: 10.1017/s0033291717002859] [Cited by in Crossref: 40] [Cited by in F6Publishing: 16] [Article Influence: 8.0] [Reference Citation Analysis]
15 Liu YS, Yang CY, Chiu PF, Lin HC, Lo CC, Lai AS, Chang CC, Lee OK. Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study. J Med Internet Res 2021;23:e27098. [PMID: 34491204 DOI: 10.2196/27098] [Reference Citation Analysis]
16 Barbieri C, Mari F, Stopper A, Gatti E, Escandell-montero P, Martínez-martínez JM, Martín-guerrero JD. A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis. Computers in Biology and Medicine 2015;61:56-61. [DOI: 10.1016/j.compbiomed.2015.03.019] [Cited by in Crossref: 30] [Cited by in F6Publishing: 22] [Article Influence: 4.3] [Reference Citation Analysis]
17 Zheng H, Ryzhov IO, Xie W, Zhong J. Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health Records. Drugs 2021;81:471-82. [PMID: 33570745 DOI: 10.1007/s40265-020-01435-4] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Syrowatka A, Song W, Amato MG, Foer D, Edrees H, Co Z, Kuznetsova M, Dulgarian S, Seger DL, Simona A, Bain PA, Purcell Jackson G, Rhee K, Bates DW. Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. Lancet Digit Health 2021:S2589-7500(21)00229-6. [PMID: 34836823 DOI: 10.1016/S2589-7500(21)00229-6] [Reference Citation Analysis]
19 Pina R, Tibebu H, Hook J, De Silva V, Kondoz A. Overcoming Challenges of Applying Reinforcement Learning for Intelligent Vehicle Control. Sensors (Basel) 2021;21:7829. [PMID: 34883832 DOI: 10.3390/s21237829] [Reference Citation Analysis]
20 Tortora M, Cordelli E, Sicilia R, Miele M, Matteucci P, Iannello G, Ramella S, Soda P. Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma. Artif Intell Med 2021;119:102137. [PMID: 34531006 DOI: 10.1016/j.artmed.2021.102137] [Reference Citation Analysis]
21 Ribba B, Dudal S, Lavé T, Peck RW. Model-Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing. Clin Pharmacol Ther 2020;107:853-7. [PMID: 31955414 DOI: 10.1002/cpt.1777] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
22 Liu Y, Qiao N, Altinel Y. Reinforcement Learning in Neurocritical and Neurosurgical Care: Principles and Possible Applications. Comput Math Methods Med 2021;2021:6657119. [PMID: 33680069 DOI: 10.1155/2021/6657119] [Reference Citation Analysis]
23 Van Laere D, Meeus M, Beirnaert C, Sonck V, Laukens K, Mahieu L, Mulder A. Machine Learning to Support Hemodynamic Intervention in the Neonatal Intensive Care Unit. Clin Perinatol 2020;47:435-48. [PMID: 32713443 DOI: 10.1016/j.clp.2020.05.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
24 Jonsson A. Deep Reinforcement Learning in Medicine. Kidney Dis (Basel) 2019;5:18-22. [PMID: 30815460 DOI: 10.1159/000492670] [Cited by in Crossref: 21] [Cited by in F6Publishing: 5] [Article Influence: 5.3] [Reference Citation Analysis]
25 Hayama Nishida CE, Costa Bianchi RA, Reali Costa AH. A framework to shift basins of attraction of gene regulatory networks through batch reinforcement learning. Artif Intell Med 2020;107:101853. [PMID: 32828434 DOI: 10.1016/j.artmed.2020.101853] [Reference Citation Analysis]
26 Coronato A, Naeem M, De Pietro G, Paragliola G. Reinforcement learning for intelligent healthcare applications: A survey. Artif Intell Med 2020;109:101964. [PMID: 34756216 DOI: 10.1016/j.artmed.2020.101964] [Cited by in Crossref: 13] [Cited by in F6Publishing: 1] [Article Influence: 6.5] [Reference Citation Analysis]
27 Hernández-del-olmo F, Gaudioso E, Dormido R, Duro N. Tackling the start-up of a reinforcement learning agent for the control of wastewater treatment plants. Knowledge-Based Systems 2018;144:9-15. [DOI: 10.1016/j.knosys.2017.12.019] [Cited by in Crossref: 9] [Cited by in F6Publishing: 1] [Article Influence: 2.3] [Reference Citation Analysis]
28 Hueso M, Vellido A, Montero N, Barbieri C, Ramos R, Angoso M, Cruzado JM, Jonsson A. Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy. Kidney Dis (Basel) 2018;4:1-9. [PMID: 29594137 DOI: 10.1159/000486394] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
29 Zhao L, Hu C, Cheng J, Zhang P, Jiang H, Chen J. Haemoglobin variability and all-cause mortality in haemodialysis patients: A systematic review and meta-analysis. Nephrology (Carlton) 2019;24:1265-72. [PMID: 30644618 DOI: 10.1111/nep.13560] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
30 Dagli MM, Rajesh A, Asaad M, Butler CE. The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review. Am Surg 2021;:31348211065101. [PMID: 34958252 DOI: 10.1177/00031348211065101] [Reference Citation Analysis]
31 Xie G, Chen T, Li Y, Chen T, Li X, Liu Z. Artificial Intelligence in Nephrology: How Can Artificial Intelligence Augment Nephrologists' Intelligence? Kidney Dis (Basel) 2020;6:1-6. [PMID: 32021868 DOI: 10.1159/000504600] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]