SMLrecommendations.bib

@article{wiemken2019machine,
  title={Machine Learning in Epidemiology and Health Outcomes Research.},
  author={Wiemken, Timothy L and Kelley, Robert R},
  journal={Annual review of public health},
  volume={41},
  pages={21--36},
  url={https://doi.org/10.1146/annurev-publhealth-040119-094437},
  year={2019}
}

@article{jiang2020supervised,
  title={Supervised machine learning: a brief primer},
  author={Jiang, Tammy and Gradus, Jaimie L and Rosellini, Anthony J},
  journal={Behavior Therapy},
  volume={51},
  number={5},
  pages={675--687},
  year={2020},
  publisher={Elsevier}
}

@article{coutanche2019machine,
  title={Machine learning for clinical psychology and clinical neuroscience},
  author={Coutanche, Marc N and Hallion, Lauren S},
  year={2019},
  publisher={PsyArXiv},
  url={http://www.mcoutanche.com/uploads/6/1/8/6/61863105/coutanche_and_hallion_2020.pdf}
}

@article{jacobucci2020machine,
  title={Machine learning and psychological research: The unexplored effect of measurement},
  author={Jacobucci, Ross and Grimm, Kevin J},
  journal={Perspectives on Psychological Science},
  volume={15},
  number={3},
  pages={809--816},
  year={2020},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}

@article{van2022validation,
  title={Validation of prediction models in the presence of competing risks: a guide through modern methods},
  author={Van Geloven, Nan and Giardiello, Daniele and Bonneville, Edouard F and Teece, Lucy and Ramspek, Chava L and Van Smeden, Maarten and Snell, Kym IE and Van Calster, Ben and Pohar-Perme, Maja and Riley, Richard D and others},
  journal={bmj},
  volume={377},
  year={2022},
  publisher={British Medical Journal Publishing Group}
}

@article{cowley2019methodological,
  title={Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature},
  author={Cowley, Laura E and Farewell, Daniel M and Maguire, Sabine and Kemp, Alison M},
  journal={Diagnostic and prognostic research},
  volume={3},
  pages={1--23},
  year={2019},
  publisher={Springer}
}

@article{navarro2021risk,
  title={Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review},
  author={Navarro, Constanza L Andaur and Damen, Johanna AA and Takada, Toshihiko and Nijman, Steven WJ and Dhiman, Paula and Ma, Jie and Collins, Gary S and Bajpai, Ram and Riley, Richard D and Moons, Karel GM and others},
  journal={bmj},
  volume={375},
  year={2021},
  publisher={British Medical Journal Publishing Group}
}

@article{andaur2022completeness,
  title={Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review},
  author={Andaur Navarro, Constanza L and Damen, Johanna AA and Takada, Toshihiko and Nijman, Steven WJ and Dhiman, Paula and Ma, Jie and Collins, Gary S and Bajpai, Ram and Riley, Richard D and Moons, Karel GM and others},
  journal={BMC medical research methodology},
  volume={22},
  pages={1--13},
  year={2022},
  publisher={Springer}
}

@article{binuya2022methodological,
  title={Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review},
  author={Binuya, MAE and Engelhardt, EG and Schats, W and Schmidt, MK and Steyerberg, EW},
  journal={BMC Medical Research Methodology},
  volume={22},
  number={1},
  pages={316},
  year={2022},
  publisher={Springer}
}

@article{steyerberg2018poor,
  title={Poor performance of clinical prediction models: the harm of commonly applied methods},
  author={Steyerberg, Ewout W and Uno, Hajime and Ioannidis, John PA and Van Calster, Ben and Ukaegbu, Chinedu and Dhingra, Tara and Syngal, Sapna and Kastrinos, Fay},
  journal={Journal of clinical epidemiology},
  volume={98},
  pages={133--143},
  year={2018},
  publisher={Elsevier}
}

@article{kompa2021second,
  title={Second opinion needed: communicating uncertainty in medical machine learning},
  author={Kompa, Benjamin and Snoek, Jasper and Beam, Andrew L},
  journal={NPJ Digital Medicine},
  volume={4},
  number={1},
  pages={4},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

@article{meehan2022clinical,
  title={Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges},
  author={Meehan, Alan J and Lewis, Stephanie J and Fazel, Seena and Fusar-Poli, Paolo and Steyerberg, Ewout W and Stahl, Daniel and Danese, Andrea},
  journal={Molecular Psychiatry},
  volume={27},
  number={6},
  pages={2700--2708},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

@article{paulus2020predictably,
  title={Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities},
  author={Paulus, Jessica K and Kent, David M},
  journal={NPJ digital medicine},
  volume={3},
  number={1},
  pages={99},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

@article{joyce2020deploying,
  title={When deploying predictive algorithms, are summary performance measures sufficient?},
  author={Joyce, Dan W and Geddes, John},
  journal={JAMA psychiatry},
  volume={77},
  number={5},
  pages={447--448},
  year={2020},
  publisher={American Medical Association}
}

@incollection{rudin2019secrets,
  title={The secrets of machine learning: Ten things you wish you had known earlier to be more effective at data analysis},
  author={Rudin, Cynthia and Carlson, David},
  booktitle={Operations Research \& Management Science in the Age of Analytics},
  pages={44--72},
  year={2019},
  publisher={Informs}
}

@article{zellner2021survey,
  title={A survey of human judgement and quantitative forecasting methods},
  author={Zellner, Maximilian and Abbas, Ali E and Budescu, David V and Galstyan, Aram},
  journal={Royal Society open science},
  volume={8},
  number={2},
  pages={201187},
  year={2021},
  publisher={The Royal Society}
}

@article{schulze2021description,
  title={A description--experience gap in statistical intuitions: Of smart babies, risk-savvy chimps, intuitive statisticians, and stupid grown-ups},
  author={Schulze, Christin and Hertwig, Ralph},
  journal={Cognition},
  volume={210},
  pages={104580},
  year={2021},
  publisher={Elsevier}
}

@article{kirtley2022translating,
  title={Translating promise into practice: a review of machine learning in suicide research and prevention},
  author={Kirtley, Olivia J and van Mens, Kasper and Hoogendoorn, Mark and Kapur, Navneet and de Beurs, Derek},
  journal={The Lancet Psychiatry},
  volume={9},
  number={3},
  pages={243--252},
  year={2022},
  publisher={Elsevier}
}

@article{shipe2019developing,
  title={Developing prediction models for clinical use using logistic regression: an overview},
  author={Shipe, Maren E and Deppen, Stephen A and Farjah, Farhood and Grogan, Eric L},
  journal={Journal of thoracic disease},
  volume={11},
  number={Suppl 4},
  pages={S574},
  year={2019},
  publisher={AME Publications}
}