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NTIS 바로가기Information systems, v.104, 2022년, pp.101878 -
Li, Tianhao , Wang, Zhishun , Lu, Wei , Zhang, Qian , Li, Dengfeng
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Ann. Transl. Med. Van Poucke 6 3 52 2018 10.21037/atm.2017.03.100 Secondary analysis of electronic health records in critical care medicine
npj Digit. Med. Rajkomar 1 2018 10.1038/s41746-018-0029-1 Scalable and accurate deep learning for electronic health records
IEEE Trans. Syst. Man Cybern. Syst. Nguyen 50 4 1318 2020 10.1109/TSMC.2017.2726547 Integrating community context information into a reliably weighted collaborative filtering system using soft ratings
Rajput 1056 2019 2019 International Conference on Data Mining Workshops, ICDM Workshops 2019, Beijing, China, November 8-11, 2019 Risk factors identification for heart disease in unstructured dataset using deep learning approach
Brief. Bioinform. Miotto 19 6 2018 10.1093/bib/bbx044 Deep learning for healthcare: review, opportunities and challenges
Nature Mnih 518 7540 529 2015 10.1038/nature14236 Human-level control through deep reinforcement learning
Artif. Intell. Med. Tejedor 104 2020 10.1016/j.artmed.2020.101836 Reinforcement learning application in diabetes blood glucose control: A systematic review
Expert Rev. Med. Devices Bothe 10 5 661 2013 10.1586/17434440.2013.827515 The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas
J. Diabetes Sci. Technol. Man 8 1 26 2014 10.1177/1932296813514502 The UVA/PADOVA type 1 diabetes simulator
E.D. Lehmann, T. Deutsch, A physiological model of glucose-insulin interaction, in: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991, 1991, pp. 2274-2275.
Fox 2019 ICML 2019 Workshop RL4RealLife Submission Reinforcement learning for blood glucose control: challenges and opportunities
Raghu vol. 68 147 2017 Proceedings of the Machine Learning for Health Care Conference, MLHC 2017, Boston, Massachusetts, USA, 18-19 August 2017 Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach
Weng 2017 Representation and reinforcement learning for personalized glycemic control in septic patients
J. Am. Med. Inform. Assoc. Xiao 25 10 1419 2018 10.1093/jamia/ocy068 Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
Nat. Silver 550 7676 354 2017 10.1038/nature24270 Mastering the game of Go without human knowledge
van Hasselt 2094 2016 Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA Deep reinforcement learning with double Q-learning
Inf. Syst. Shaw 2021 Applying reinforcement learning towards automating energy efficient virtual machine consolidation in cloud data centers
Nemati 2016 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach
Prasad 2017 A reinforcement learning approach to weaning of mechanical ventilation in intensive care units
Sensors Zhu 20 18 5058 2020 10.3390/s20185058 An insulin bolus advisor for type 1 diabetes using deep reinforcement learning
Expert Syst. Appl. Paula 42 4 2234 2015 10.1016/j.eswa.2014.10.038 On-line policy learning and adaptation for real-time personalization of an artificial pancreas
Appl. Soft Comput. Paula 35 310 2015 10.1016/j.asoc.2015.06.041 Controlling blood glucose variability under uncertainty using reinforcement learning and Gaussian processes
Sutton 1057 1999 Proceedings of the 12th International Conference on Neural Information Processing Systems Policy gradient methods for reinforcement learning with function approximation
Silver 2014 31st International Conference on Machine Learning, ICML 2014, Vol. 1 Deterministic policy gradient algorithms
PLOS ONE Daskalaki 11 7 2016 10.1371/journal.pone.0158722 Model-free machine learning in biomedicine: Feasibility study in type 1 diabetes
Comput. Methods Programs Biomed. Daskalaki 109 2 116 2013 10.1016/j.cmpb.2012.03.002 An Actor-Critic based controller for glucose regulation in type 1 diabetes
Expert Syst. Appl. Avila 41 14 6327 2014 10.1016/j.eswa.2014.04.031 Behavior monitoring under uncertainty using Bayesian surprise and optimal action selection
Adv. Eng. Inform. Avila 29 4 1083 2015 10.1016/j.aei.2015.07.008 An active inference approach to on-line agent monitoring in safety-critical systems
Control Eng. Pract. Torkestani 26 151 2014 10.1016/j.conengprac.2014.01.010 A learning automata-based blood glucose regulation mechanism in type 2 diabetes
Patil 313 2014 Advances in Intelligent Systems and Computing Sequential decision making using q learning algorithm for diabetic patients
Pan 2020 Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, Virtual Trust the model when it is confident: Masked model-based actor-critic
J. Amer. Statist. Assoc. Luckett 115 530 692 2019 10.1080/01621459.2018.1537919 Estimating dynamic treatment regimes in mobile health using V-Learning
PLOS ONE Tampuu 12 4 1 2017 10.1371/journal.pone.0172395 Multiagent cooperation and competition with deep reinforcement learning
Sunehag 2085 2018 Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, July 10-15, 2018 Value-decomposition networks for cooperative multi-agent learning based on team reward
Ma 2021 Modeling the interaction between agents in cooperative multi-agent reinforcement learning
Janner 12498 2019 Advances in Neural Information Processing Systems 32 (NIPS 2019) When to trust your model: Model-based policy optimization
Castellini 1862 2019 Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems The representational capacity of action-value networks for multi-agent reinforcement learning
Son vol. 97 5887 2019 Proceedings of the 36th International Conference on Machine Learning QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Diabetes Metab. Syndr.: Clin. Res. Rev. Pal 14 6 1563 2020 10.1016/j.dsx.2020.08.015 Clinical profile and outcomes in COVID-19 patients with diabetic ketoacidosis: A systematic review of literature
Metabolism Chamorro-Pareja 110 2020 10.1016/j.metabol.2020.154301 Letter to the editor: Unexpected high mortality in COVID-19 and diabetic ketoacidosis
Pediatr. Diabetes Cameron 19 S27 250 2018 10.1111/pedi.12702 ISPAD clinical practice consensus guidelines 2018: Diabetes in adolescence
Nutr. Metab. Cardiovasc. Dis. Lapolla 30 10 1633 2020 10.1016/j.numecd.2020.06.006 Diabetic ketoacidosis: A consensus statement of the Italian Association of Medical Diabetologists (AMD), Italian Society of Diabetology (SID), Italian Society of Endocrinology and Pediatric Diabetoloy (SIEDP)
Sci. Data Johnson 3 1 2016 10.1038/sdata.2016.35 MIMIC-III, a freely accessible critical care database
Diabetes Care Ramphul 43 12 e196 2020 10.2337/dc20-1258 An update on the incidence and burden of diabetic ketoacidosis in the U.S.
Liu 2017 2017 IEEE International Conference on Healthcare Informatics (ICHI) Deep reinforcement learning for dynamic treatment regimes on medical registry data
R J. Scrucca 8 1 205 2016 10.32614/RJ-2016-021 Mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models
Clin. Ther. Vidyasagar 42 8 e115 2020 10.1016/j.clinthera.2020.06.017 Efficacy and safety of commonly used insulin analogues in the treatment of diabetic ketoacidosis: A Bayesian indirect treatment comparison
Dewey 2014 2014 AAAI Spring Symposium Series Reinforcement learning and the reward engineering principle
IEEE J. Biomed. Health Inform. Zhu 1 2020 Basal glucose control in type 1 diabetes using deep reinforcement learning: An in silico validation
Mach. Learn. Watkins 8 3-4 279 1992 10.1007/BF00992698 Q-learning
Comput. Sci. Hasselt 2015 Deep reinforcement learning with double Q-learning
Wang 1995 2016 International Conference on Machine Learning Dueling network architectures for deep reinforcement learning
Informatics Syed 8 16 2021 Informatics application of machine learning in intensive care unit (ICU) settings using MIMIC dataset: Systematic review
Artif. Intell. Med. Mt 104 2020 Reinforcement learning application in diabetes blood glucose control: A systematic review
Nguyen 2018 Deep reinforcement learning for multi-agent systems: A review of challenges, solutions and applications
J. Healthc. Inform. Res. Jeon 4 11 2019 Predicting glycaemia in type 1 diabetes patients: Experiments in feature engineering and data imputation
IEEE Access Khan PP 99 1 2021 Detection and prediction of diabetes using data mining: A comprehensive review
Anand 310 2018 AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science, Vol. 2017 Predicting mortality in diabetic ICU patients using machine learning and severity indices
IEEE Trans. Biomed. Eng. Noaro 68 1 247 2021 10.1109/TBME.2020.3004031 Machine-learning based model to improve insulin bolus calculation in type 1 diabetes therapy
Martinez 3960 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin, Germany, July 23-27, 2019 Deep reinforcement learning for optimal critical care pain management with morphine using dueling double-deep q networks
Liu 1 2020 8th IEEE International Conference on Healthcare Informatics, ICHI 2020, Oldenburg, Germany, November 30 - December 3, 2020 A deep reinforcement learning approach for type 2 diabetes mellitus treatment
Myhre 1 2018 28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018, Aalborg, Denmark, September 17-20, 2018 Controlling blood glucose levels in patients with type 1 diabetes using fitted Q-iterations and functional features
Rashid 2020 Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, Virtual Weighted QMIX: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning
Son vol. 97 5887 2019 Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
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