150 related articles for article (PubMed ID: 28268938)
1. Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach.
Nemati S; Ghassemi MM; Clifford GD
Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():2978-2981. PubMed ID: 28268938
[TBL] [Abstract][Full Text] [Related]
2. A Deep Deterministic Policy Gradient Approach to Medication Dosing and Surveillance in the ICU.
Lin R; Stanley MD; Ghassemi MM; Nemati S
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():4927-4931. PubMed ID: 30441448
[TBL] [Abstract][Full Text] [Related]
3. Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units.
Yu C; Liu J; Zhao H
BMC Med Inform Decis Mak; 2019 Apr; 19(Suppl 2):57. PubMed ID: 30961594
[TBL] [Abstract][Full Text] [Related]
4. Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks.
Lopez-Martinez D; Eschenfeldt P; Ostvar S; Ingram M; Hur C; Picard R
Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():3960-3963. PubMed ID: 31946739
[TBL] [Abstract][Full Text] [Related]
5. Continuous action deep reinforcement learning for propofol dosing during general anesthesia.
Schamberg G; Badgeley M; Meschede-Krasa B; Kwon O; Brown EN
Artif Intell Med; 2022 Jan; 123():102227. PubMed ID: 34998516
[TBL] [Abstract][Full Text] [Related]
6. Patient-Specific Sedation Management via Deep Reinforcement Learning.
Eghbali N; Alhanai T; Ghassemi MM
Front Digit Health; 2021; 3():608893. PubMed ID: 34713090
[No Abstract] [Full Text] [Related]
7. A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients.
Lin T; Zhang X; Gong J; Tan R; Li W; Wang L; Pan Y; Xu X; Gao J
BMC Med Inform Decis Mak; 2023 May; 23(1):81. PubMed ID: 37143048
[TBL] [Abstract][Full Text] [Related]
8. Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study.
Su L; Liu C; Li D; He J; Zheng F; Jiang H; Wang H; Gong M; Hong N; Zhu W; Long Y
JMIR Med Inform; 2020 Jun; 8(6):e17648. PubMed ID: 32568089
[TBL] [Abstract][Full Text] [Related]
9. Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis.
Roggeveen L; El Hassouni A; Ahrendt J; Guo T; Fleuren L; Thoral P; Girbes AR; Hoogendoorn M; Elbers PW
Artif Intell Med; 2021 Feb; 112():102003. PubMed ID: 33581824
[TBL] [Abstract][Full Text] [Related]
10. A Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study.
Li D; Gao J; Hong N; Wang H; Su L; Liu C; He J; Jiang H; Wang Q; Long Y; Zhu W
J Med Internet Res; 2021 May; 23(5):e27118. PubMed ID: 34014171
[TBL] [Abstract][Full Text] [Related]
11. Optimization of anemia treatment in hemodialysis patients via reinforcement learning.
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
Artif Intell Med; 2014 Sep; 62(1):47-60. PubMed ID: 25091172
[TBL] [Abstract][Full Text] [Related]
12. Model enhanced reinforcement learning to enable precision dosing: A theoretical case study with dosing of propofol.
Ribba B; Bräm DS; Baverel PG; Peck RW
CPT Pharmacometrics Syst Pharmacol; 2022 Nov; 11(11):1497-1510. PubMed ID: 36177959
[TBL] [Abstract][Full Text] [Related]
13. Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units.
Yu C; Ren G; Dong Y
BMC Med Inform Decis Mak; 2020 Jul; 20(Suppl 3):124. PubMed ID: 32646412
[TBL] [Abstract][Full Text] [Related]
14. Parameterized MDPs and Reinforcement Learning Problems-A Maximum Entropy Principle-Based Framework.
Srivastava A; Salapaka SM
IEEE Trans Cybern; 2022 Sep; 52(9):9339-9351. PubMed ID: 34406959
[TBL] [Abstract][Full Text] [Related]
15. Benchmarking deep learning models on large healthcare datasets.
Purushotham S; Meng C; Che Z; Liu Y
J Biomed Inform; 2018 Jul; 83():112-134. PubMed ID: 29879470
[TBL] [Abstract][Full Text] [Related]
16. Deep reinforcement learning for automated radiation adaptation in lung cancer.
Tseng HH; Luo Y; Cui S; Chien JT; Ten Haken RK; Naqa IE
Med Phys; 2017 Dec; 44(12):6690-6705. PubMed ID: 29034482
[TBL] [Abstract][Full Text] [Related]
17. Reinforcement Learning in Neurocritical and Neurosurgical Care: Principles and Possible Applications.
Liu Y; Qiao N; Altinel Y
Comput Math Methods Med; 2021; 2021():6657119. PubMed ID: 33680069
[TBL] [Abstract][Full Text] [Related]
18. Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV.
Yu C; Dong Y; Liu J; Ren G
BMC Med Inform Decis Mak; 2019 Apr; 19(Suppl 2):60. PubMed ID: 30961606
[TBL] [Abstract][Full Text] [Related]
19. A unified analysis of value-function-based reinforcement- learning algorithms.
Szepesvári C; Littman ML
Neural Comput; 1999 Nov; 11(8):2017-59. PubMed ID: 10578043
[TBL] [Abstract][Full Text] [Related]
20. A four-year experience with patient individualized heparin and protamine dosing using the Hemochron RxDx system.
Bennett KM; Briggins D; Zucker M; LaDuca F
J Extra Corpor Technol; 2001 Feb; 33(1):19-22. PubMed ID: 11315128
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
