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    number = {16},
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@article{hindie2020,
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@article{kudura2021,
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address={},
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@book{Press1992,
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  Year                    ={1992},
  Address                 ={Cambridge, USA},
  Edition                 ={Second}
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@article{Huff2021,
doi = {10.1088/2057-1976/ac27c3},
url = {https://dx.doi.org/10.1088/2057-1976/ac27c3},
year = {2021},
month = {sep},
publisher = {IOP Publishing},
volume = {7},
number = {6},
pages = {065019},
author = {Huff, Daniel T and Ferjancic, Peter and Namías, Mauro and Emamekhoo, Hamid and Perlman, Scott B and Jeraj, Robert},
title = {Image intensity histograms as imaging biomarkers: application to immune-related colitis},
journal = {Biomedical Physics \& Engineering Express}
}


@article{robert2021,
  title={Long-term safety of pembrolizumab monotherapy and relationship with clinical outcome: A landmark analysis in patients with advanced melanoma},
  author={Caroline Robert and Wen-Jen Hwu and Omid Hamid and Antoni Ribas and Jeffrey S. Weber and Adil I. Daud and F. Stephen Hodi and Jedd D. Wolchok and Tara C. Mitchell and Peter Hersey and Roxana Dronca and Richard W. Joseph and Celine Boutros and Le Min and Georgina V. Long and Jacob Schachter and Igor Puzanov and Reinhard Dummer and Jianxin Lin and Nageatte Ibrahim and Scott J. Diede and Matteo S. Carlino and Anthony M. Joshua},
  journal={European Journal of Cancer},
  volume={144},
  pages={182--191},
  year={2021},
  issn={0959-8049},
  doi={https://doi.org/10.1016/j.ejca.2020.11.010},
  url={https://www.sciencedirect.com/science/article/pii/S0959804920313332},
  keywords={Pembrolizumab, Advanced melanoma, Immune-related adverse events, Immune-checkpoint inhibitors, PD-1 inhibitors, Immunomodulating drugs, Corticosteroid use},
  abstract={This study evaluates the long-term safety and clinical outcomes of pembrolizumab monotherapy in patients with advanced melanoma, using data from KEYNOTE-001, KEYNOTE-002, and KEYNOTE-006. Adverse events (AEs) were analyzed for 1567 patients with a median follow-up of 42.4 months. Most AEs were mild or moderate, with grade 3/4 treatment-related AEs occurring in 17.7% of patients. Any-grade immune-mediated AEs (imAEs) were reported in 23.0% of patients, with hypothyroidism, pneumonitis, and hyperthyroidism being the most common. Landmark analysis showed similar efficacy outcomes regardless of the presence of imAEs or systemic corticosteroid use. The findings reinforce the safety profile of pembrolizumab without new toxicity signals after extended follow-up.},
  clinicaltrial={NCT01295827, NCT01704287, NCT01866319}
}


@article{li2023challenges,
  title={Challenges and opportunities in molecular simulation of amorphous polymer materials},
  author={Li, Yang and Jiang, Bin},
  journal={Chinese Journal of Chemical Physics},
  volume={36},
  number={6},
  pages={705--714},
  year={2023},
  publisher={AIP Publishing},
  abstract={This paper discusses the current challenges and future opportunities in molecular simulation of amorphous polymer materials, focusing on computational methods and material properties.}
}

@article{cheng2022accelerating,
  title={Accelerating molecular simulations of soft materials using machine learning},
  author={Cheng, Bingqing and Chen, Mojie and Ceriotti, Michele and others},
  journal={Nature Reviews Materials},
  volume={7},
  number={11},
  pages={822--833},
  year={2022},
  publisher={Nature Publishing Group},
  abstract={The application of machine learning techniques in molecular simulations of soft materials is reviewed, highlighting efficiency gains and predictive accuracy.}
}

@article{zhang2021machine,
  title={Machine learning in polymer informatics},
  author={Zhang, Yongjin and Wang, Hengbin and Luo, Yingkai and Wang, Chongmin and Han, Jinkyu and Lin, Taoran and others},
  journal={InfoMat},
  volume={3},
  number={9},
  pages={943--965},
  year={2021},
  publisher={Wiley Online Library},
  abstract={An overview of recent developments in machine learning for polymer informatics, focusing on data-driven approaches for polymer design and property prediction.}
}

@article{behler2016perspective,
  title={Perspective: Machine learning potentials for atomistic simulations},
  author={Behler, J{\"o}rg},
  journal={The Journal of Chemical Physics},
  volume={145},
  number={17},
  pages={170901},
  year={2016},
  publisher={AIP Publishing},
  abstract={This perspective article reviews machine learning-based interatomic potentials and their impact on atomistic simulations, covering methodologies and applications.}
}


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@book{wilks2011statistical,
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@article{toma2014quantitative,
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@book{pepe2003statistical,
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@book{zhou2014statistical,
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@article{richter2018review,
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}

@book{steyerberg,
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@article{kim2012development,
  title={Development of novel breast cancer recurrence prediction model using support vector machine},
  author={Kim, Woojae and Kim, Ku Sang and Lee, Jeong Eon and Noh, Dong-Young and Kim, Sung-Won and Jung, Yong Sik and Park, Man Young and Park, Rae Woong},
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  number={2},
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@article{bellazzi2008predictive,
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@article{yu2016development,
  title={Development and validation of a prediction model to estimate individual risk of pancreatic cancer},
  author={Yu, Ami and Woo, Sang Myung and Joo, Jungnam and Yang, Hye-Ryung and Lee, Woo Jin and Park, Sang-Jae and Nam, Byung-Ho},
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  year={2016},
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}

@article{janes2008assessing,
  title={Assessing the value of risk predictions by using risk stratification tables},
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  number={10},
  pages={751--760},
  year={2008},
  publisher={American College of Physicians}
}

@article{cook2008statistical,
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  number={1},
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  year={2008},
  publisher={Oxford University Press}
}

@article{pepe2010potential,
  title={The potential of genes and other markers to inform about risk},
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  journal={Cancer epidemiology, biomarkers \& prevention},
  volume={19},
  number={3},
  pages={655--665},
  year={2010},
  publisher={AACR}
}

@article{huang2009parametric,
  title={A parametric roc model-based approach for evaluating the predictiveness of continuous markers in case--control studies},
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  number={4},
  pages={1133--1144},
  year={2009},
  publisher={Oxford University Press}
}

@article{lineberry2016recommendations,
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  journal={BMJ},
  volume={355},
  year={2016},
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  doi={https://doi.org/10.1136/bmj.i5078}
}

@book{cox2018analysis,
  title={Analysis of survival data},
  author={Cox, David Roxbee},
  year={2018},
  publisher={Chapman and Hall/CRC}
}

@article{khoshgoftaar1999logistic,
  title={Logistic regression modeling of software quality},
  author={Khoshgoftaar, Taghi M and Allen, Edward B},
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  number={04},
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  title={Logistic regression},
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  year={2007},
  publisher={Springer}
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@article{pillans2008clinical,
  title={Clinical perspectives in drug safety and adverse drug reactions},
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  volume={1},
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  year={2008},
  publisher={Taylor \& Francis}
}

@article{murff2003detecting,
  title={Detecting adverse events for patient safety research: a review of current methodologies},
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  journal={Journal of biomedical informatics},
  volume={36},
  number={1-2},
  pages={131--143},
  year={2003},
  publisher={Elsevier}
}

@article{coz2024overview,
  title={An Overview of Regression Models for Adverse Events Analysis},
  author={Coz, Elsa and Fauvernier, Mathieu and Maucort-Boulch, Delphine},
  journal={Drug Safety},
  volume={47},
  number={3},
  pages={205--216},
  year={2024},
  publisher={Springer}
}

@article{zou2023review,
  title={A review of uncertainty estimation and its application in medical imaging},
  author={Zou, Ke and Chen, Zhihao and Yuan, Xuedong and Shen, Xiaojing and Wang, Meng and Fu, Huazhu},
  journal={Meta-Radiology},
  pages={100003},
  year={2023},
  publisher={Elsevier}
}

@article{abdar2021review,
  title={A review of uncertainty quantification in deep learning: Techniques, applications and challenges},
  author={Abdar, Moloud and Pourpanah, Farhad and Hussain, Sadiq and Rezazadegan, Dana and Liu, Li and Ghavamzadeh, Mohammad and Fieguth, Paul and Cao, Xiaochun and Khosravi, Abbas and Acharya, U Rajendra and others},
  journal={Information fusion},
  volume={76},
  pages={243--297},
  year={2021},
  publisher={Elsevier}
}

@book{benjamin2014probability,
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}
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    title = {On the treatment of uncertainties and probabilities in engineering decision analysis},
    abstract = {In the present paper an introduction is initially given on the interpretation of uncertainty and probability in engineering decision analysis and it is explained how, in some cases, uncertainties may change type depending on the "scale" of the applied modeling and as a function of time. Thereafter it is attempted to identify and outline the generic character of different engineering decision problems and to categorize these as prior, posterior, and preposterior decision problems, in accordance with the Bayesian decision theory. Finally, input is given to an ongoing discussion concerning the correctness and consistency of uncertainty modeling applied in the most recent reliability updating analysis for structural requalification and inspection and maintenance planning. To this end an outline is given in regard to appropriate uncertainty treatment in the probabilistic modeling for different types of decision problems.},
    author = {Faber, Michael Havbro},
    year = {2005},
    month = aug,
    doi = {10.1115/1.1951776},
    language = {English},
    volume = {127},
    pages = {243--248},
    journal = {Journal of Offshore Mechanics and Arctic Engineering},
    issn = {0892-7219},
    publisher = {American Society of Mechanical Engineers},
    number = {3},
}

@article{lindley2000philosophy,
  title={The philosophy of statistics},
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  year={2007},
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@article{kiureghian1989measures,
  title={Measures of structural safety under imperfect states of knowledge},
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@book{Johnson1994,
   title =     {Continuous Univariate Distributions, Vol. 1},
   author =    {Norman L. Johnson and Samuel Kotz and  Narayanaswamy Balakrishnan},
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   volume =    {}
}

@book{Johnson1995,
   title =     {Continuous univariate distributions, Vol. 2},
   author =    {Norman L. Johnson and Samuel Kotz and Narayanaswamy Balakrishnan },
   publisher = {John Wiley \& Sons, Inc.},
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   year =      {1995},
   series =    {Wiley Series in Probability and Statistics},
   edition =   {2},
   volume =    {}
}

