ML_Assignment_Bibliography.bib

@INPROCEEDINGS{6137280,
  author={Wallace, Byron C. and Small, Kevin and Brodley, Carla E. and Trikalinos, Thomas A.},
  booktitle={2011 IEEE 11th International Conference on Data Mining}, 
  title={Class Imbalance, Redux}, 
  year={2011},
  volume={},
  number={},
  pages={754-763},
  doi={10.1109/ICDM.2011.33}}

@Inbook{ElNaqa2015,
author="El Naqa, Issam
and Murphy, Martin J.",
editor="El Naqa, Issam
and Li, Ruijiang
and Murphy, Martin J.",
title="What Is Machine Learning?",
bookTitle="Machine Learning in Radiation Oncology: Theory and Applications",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="3--11",
abstract="Machine learning is an evolving branch of computational algorithms that are designed to emulate human intelligence by learning from the surrounding environment. They are considered the working horse in the new era of the so-called big data. Techniques based on machine learning have been applied successfully in diverse fields ranging from pattern recognition, computer vision, spacecraft engineering, finance, entertainment, and computational biology to biomedical and medical applications. More than half of the patients with cancer receive ionizing radiation (radiotherapy) as part of their treatment, and it is the main treatment modality at advanced stages of local disease. Radiotherapy involves a large set of processes that not only span the period from consultation to treatment but also extend beyond that to ensure that the patients have received the prescribed radiation dose and are responding well. The degrees of the complexity of these processes can vary and may involve several stages of sophisticated human-machine interactions and decision making, which would naturally invite the use of machine learning algorithms into optimizing and automating these processes including but not limited to radiation physics quality assurance, contouring and treatment planning, image-guided radiotherapy, respiratory motion management, treatment response modeling, and outcomes prediction. The ability of machine learning algorithms to learn from current context and generalize into unseen tasks would allow improvements in both the safety and efficacy of radiotherapy practice leading to better outcomes.",
isbn="978-3-319-18305-3",
doi="10.1007/978-3-319-18305-3_1",
url="https://doi.org/10.1007/978-3-319-18305-3_1"
}


@Inbook{GovIsrael2021,
author="Israel Ministry of Health",
title= "Collected COVID-19 data",
year="2021",
url="https://data.gov.il/dataset/covid-19",
url="https://data.gov.il/dataset/covid-19/resource/3f5c975e-7196-454b-8c5b-ef85881f78db?inner_span=True"
}

@article{doi:10.1056/NEJMc2001737,
author = {Zou, Lirong and Ruan, Feng and Huang, Mingxing and Liang, Lijun and Huang, Huitao and Hong, Zhongsi and Yu, Jianxiang and Kang, Min and Song, Yingchao and Xia, Jinyu and Guo, Qianfang and Song, Tie and He, Jianfeng and Yen, Hui-Ling and Peiris, Malik and Wu, Jie},
title = {SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients},
journal = {New England Journal of Medicine},
volume = {382},
number = {12},
pages = {1177-1179},
year = {2020},
doi = {10.1056/NEJMc2001737},
    note ={PMID: 32074444},
URL = {https://doi.org/10.1056/NEJMc2001737},
eprint = {https://doi.org/10.1056/NEJMc2001737}
}


@Article{info:doi/10.2196/27254,
author="Mbwogge, Mathew",
title="Mass Testing With Contact Tracing Compared to Test and Trace for the Effective Suppression of COVID-19 in the United Kingdom: Systematic Review",
journal="JMIRx Med",
year="2021",
month="Apr",
day="12",
volume="2",
number="2",
pages="e27254",
keywords="COVID-19; SARS-CoV-2; test and trace; universal testing; mass testing; contact tracing; infection surveillance; prevention and control; review",
abstract="Background: Making testing available to everyone and tracing contacts might be the gold standard to control COVID-19. Many countries including the United Kingdom have relied on the symptom-based test and trace strategy in bringing the COVID-19 pandemic under control. The effectiveness of a test and trace strategy based on symptoms has been questionable and has failed to meet testing and tracing needs. This is further exacerbated by it not being delivered at the point of care, leading to rising cases and deaths. Increases in COVID-19 cases and deaths in the United Kingdom despite performing the highest number of tests in Europe suggest that symptom-based testing and contact tracing might not be effective as a control strategy. An alternative strategy is making testing available to all. Objective: The primary objective of this review was to compare mass testing and contact tracing with the conventional test and trace method in the suppression of SARS-CoV-2 infections. The secondary objective was to determine the proportion of asymptomatic COVID-19 cases reported during mass testing interventions. Methods: Literature in English was searched from September through December 2020 in Google Scholar, ScienceDirect, Mendeley, and PubMed. Search terms included ``mass testing,'' ``test and trace,'' ``contact tracing,'' ``COVID-19,'' ``SARS-CoV-2,'' ``effectiveness,'' ``asymptomatic,'' ``symptomatic,'' ``community screening,'' ``UK,'' and ``2020.'' Search results were synthesized without meta-analysis using the direction of effect as the standardized metric and vote counting as the synthesis metric. A statistical synthesis was performed using Stata 14.2. Tabular and graphical methods were used to present findings. Results: The literature search yielded 286 articles from Google Scholar, 20 from ScienceDirect, 14 from Mendeley, 27 from PubMed, and 15 through manual search. A total of 35 articles were included in the review, with a sample size of nearly 1 million participants. We found a 76.9{\%} (10/13, 95{\%} CI 46.2{\%}-95.0{\%}; P=.09) majority vote in favor of the intervention under the primary objective. The overall proportion of asymptomatic cases among those who tested positive and in the tested sample populations under the secondary objective was 40.7{\%} (1084/2661, 95{\%} CI 38.9{\%}-42.6{\%}) and 0.0{\%} (1084/9,942,878, 95{\%} CI 0.0{\%}-0.0{\%}), respectively. Conclusions: There was low-level but promising evidence that mass testing and contact tracing could be more effective in bringing the virus under control and even more effective if combined with social distancing and face coverings. The conventional test and trace method should be superseded by decentralized and regular mass rapid testing and contact tracing, championed by general practitioner surgeries and low-cost community services. ",
issn="2563-6316",
doi="10.2196/27254",
url="https://xmed.jmir.org/2021/2/e27254",
url="https://doi.org/10.2196/27254",
url="http://www.ncbi.nlm.nih.gov/pubmed/33857269"
}


@inbook{inbook,
author = {Kearney, Michael},
year = {2017},
month = {12},
pages = {},
title = {Cramér's V},
doi = {10.4135/9781483381411.n107}
}




@article{10.1214/ss/1009213726,
author = {Leo Breiman},
title = {{Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)}},
volume = {16},
journal = {Statistical Science},
number = {3},
publisher = {Institute of Mathematical Statistics},
pages = {199 -- 231},
year = {2001},
doi = {10.1214/ss/1009213726},
URL = {https://doi.org/10.1214/ss/1009213726}
}