refs.bib

@article{cpic,
author = {Johnson, JA and Caudle, KE and Gong, L and Whirl-Carrillo, M and Stein, CM and Scott, SA and Lee, MT and Gage, BF and Kimmel, SE and Perera, MA and Anderson, JL and Pirmohamed, M and Klein, TE and Limdi, NA and Cavallari, LH and Wadelius, M},
title = {Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Pharmacogenetics-Guided Warfarin Dosing: 2017 Update},
journal = {Clinical Pharmacology \& Therapeutics},
volume = {102},
number = {3},
pages = {397-404},
doi = {doi.org/10.1002/cpt.668},
url = {https://ascpt.onlinelibrary.wiley.com/doi/abs/10.1002/cpt.668},
eprint = {https://ascpt.onlinelibrary.wiley.com/doi/pdf/10.1002/cpt.668},
abstract = {This document is an update to the 2011 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2C9 and VKORC1 genotypes and warfarin dosing. Evidence from the published literature is presented for CYP2C9, VKORC1, CYP4F2, and rs12777823 genotype-guided warfarin dosing to achieve a target international normalized ratio of 2–3 when clinical genotype results are available. In addition, this updated guideline incorporates recommendations for adult and pediatric patients that are specific to continental ancestry.},
year = {2017},
urldate = {2024-05-05},
}


@article{warfarin1,
	title = {{Warfarin - PharmGKB}},
	url = {https://www.pharmgkb.org/chemical/PA451906},
	urldate = {2024-05-05},
}

@article{warfarin-data,
	title = {{Warfarin Data - PharmGKB}},
	url = {https://api.pharmgkb.org/v1/download/submission/553247439},
	urldate = {2024-05-05},
}

@article{rl,
	title = {{Reinforcement learning a brief guide - Mathworks}},
	url = {https://www.mathworks.com/company/newsletters/articles/reinforcement-learning-a-brief-guideum.jpg},
	urldate = {2024-05-05},
}

@article{nhgri,
	title = {{Talking Glossary of Genetic Terms | NHGRI}},
	url = {https://www.genome.gov/genetics-glossary},
	urldate = {2024-05-05},
}

@article{base,
  author       = {Arpita Vats},
  title        = {Estimation of Warfarin Dosage with Reinforcement Learning},
  journal      = {CoRR},
  volume       = {abs/2109.07564},
  year         = {2021},
  url          = {https://arxiv.org/abs/2109.07564},
urldate = {2024-05-05},
  eprinttype    = {arXiv},
  eprint       = {2109.07564},
  timestamp    = {Wed, 22 Sep 2021 14:16:57 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2109-07564.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{linucb,
author = {Li, Lihong and Chu, Wei and Langford, John and Schapire, Robert E.},
title = {A contextual-bandit approach to personalized news article recommendation},
year = {2010},
isbn = {9781605587998},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1772690.1772758},
doi = {doi.org/10.1145/1772690.1772758},
abstract = {Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation.In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5\% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.},
booktitle = {Proceedings of the 19th International Conference on World Wide Web},
pages = {661–670},
numpages = {10},
keywords = {web service, recommender systems, personalization, exploration/exploitation dilemma, contextual bandit},
location = {Raleigh, North Carolina, USA},
series = {WWW '10}
}

@book{sinh12,
  title     = "Sinh học 12",
  author    = "Nguyễn Thành Đạt and Phạm Văn Lập and Đặng Hữu Lanh and Mai Sỹ Tuấn",
  year      = 2014,
  publisher = "Nhà xuất bản Giáo Dục Việt Nam",
  edition = 6
}

@article{bandit,
    author = {Tor Lattimore and Csaba Szepesvári},
    title = {Bandit algorithms},
    url = {https://tor-lattimore.com/downloads/book/book.pdf},
    urldate = {2024-05-05},
}

@article{linucb-base,
    author       = {Thomas J. Walsh and
                  Istvan Szita and
                  Carlos Diuk and
                  Michael L. Littman},
    title        = {Exploring compact reinforcement-learning representations with linear
                  regression},
    journal      = {CoRR},
    volume       = {abs/1205.2606},
    year         = {2012},
    url          = {http://arxiv.org/abs/1205.2606},
    eprinttype    = {arXiv},
    eprint       = {1205.2606},
    timestamp    = {Tue, 15 Nov 2022 10:44:41 +0100},
    biburl       = {https://dblp.org/rec/journals/corr/abs-1205-2606.bib},
    bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{inr,
    author = {Kuruvilla, Mariamma and Gurk-Turner, Cheryle},
    journal = {Proceedings - Baylor University. Medical Center},
    month = {7},
    number = {3},
    pages = {305--306},
    title = {{A review of Warfarin dosing and monitoring}},
    volume = {14},
    year = {2001},
    doi = {10.1080/08998280.2001.11927781},
    url = {https://doi.org/10.1080/08998280.2001.11927781},
}