
@article{rajkomar_scalable_2018,
  title        = {Scalable and accurate deep learning with electronic health records},
  volume       = {1},
  rights       = {2018 The Author(s)},
  issn         = {2398-6352},
  url          = {https://www.nature.com/articles/s41746-018-0029-1/},
  doi          = {10.1038/s41746-018-0029-1},
  abstract     = {Predictive modeling with electronic health record ({EHR}) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized {EHR} data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation of patients’ entire raw {EHR} records based on the Fast Healthcare Interoperability Resources ({FHIR}) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified {EHR} data from two {US} academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of {EHR} data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [{AUROC}] across sites 0.93–0.94), 30-day unplanned readmission ({AUROC} 0.75–0.76), prolonged length of stay ({AUROC} 0.85–0.86), and all of a patient’s final discharge diagnoses (frequency-weighted {AUROC} 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient’s chart.},
  pages        = {1--10},
  number       = {1},
  journaltitle = {npj Digital Medicine},
  author       = {Rajkomar, Alvin and Oren, Eyal and Chen, Kai and Dai, Andrew M. and Hajaj, Nissan and Hardt, Michaela and Liu, Peter J. and Liu, Xiaobing and Marcus, Jake and Sun, Mimi and Sundberg, Patrik and Yee, Hector and Zhang, Kun and Zhang, Yi and Flores, Gerardo and Duggan, Gavin E. and Irvine, Jamie and Le, Quoc and Litsch, Kurt and Mossin, Alexander and Tansuwan, Justin and Wang, De and Wexler, James and Wilson, Jimbo and Ludwig, Dana and Volchenboum, Samuel L. and Chou, Katherine and Pearson, Michael and Madabushi, Srinivasan and Shah, Nigam H. and Butte, Atul J. and Howell, Michael D. and Cui, Claire and Corrado, Greg S. and Dean, Jeffrey},
  urldate      = {2020-09-13},
  date         = {2018-05-08},
  langid       = {english},
  note         = {Number: 1
                  Publisher: Nature Publishing Group},
  file         = {Full Text PDF:/Users/au554730/Zotero/storage/XTCIFGTX/Rajkomar et al. - 2018 - Scalable and accurate deep learning with electroni.pdf:application/pdf;Snapshot:/Users/au554730/Zotero/storage/DC6GJ9PM/s41746-018-0029-1.html:text/html}
}

@article{hansen_psychiatric_2021,
  title        = {The {PSYchiatric} clinical outcome prediction ({PSYCOP}) cohort: leveraging the potential of electronic health records in the treatment of mental disorders},
  issn         = {0924-2708, 1601-5215},
  url          = {https://www.cambridge.org/core/journals/acta-neuropsychiatrica/article/psychiatric-clinical-outcome-prediction-psycop-cohort-leveraging-the-potential-of-electronic-health-records-in-the-treatment-of-mental-disorders/73CDCC5B36FF1347E6419EC7B80DEC48},
  doi          = {10.1017/neu.2021.22},
  shorttitle   = {The {PSYchiatric} clinical outcome prediction ({PSYCOP}) cohort},
  abstract     = {Background:
                  The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalised predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes. To overcome these challenges, we have established the {PSYchiatric} Clinical Outcome Prediction ({PSYCOP}) cohort, which will form the basis for extensive studies in the upcoming years.
                  
                  
                  Methods:
                  {PSYCOP} is a retrospective cohort study that includes all patients with at least one contact with the psychiatric services of the Central Denmark Region in the period from January 1, 2011, to October 28, 2020 (n = 119 291). All data from the electronic health records ({EHR}) are included, spanning diagnoses, information on treatments, clinical notes, discharge summaries, laboratory tests, etc. Based on these data, machine learning methods will be used to make prediction models for a range of clinical outcomes, such as diagnostic shifts, treatment response, medical comorbidity, and premature mortality, with an explicit focus on clinical feasibility and implementation.
                  
                  
                  Discussions:
                  We expect that studies based on the {PSYCOP} cohort will advance the field of precision psychiatry through the use of state-of-the-art machine learning methods on a large and representative data set. Implementation of prediction models in clinical psychiatry will likely improve treatment and, hopefully, increase the quality of life and lifespan of those with mental illness.},
  pages        = {1--8},
  journaltitle = {Acta Neuropsychiatrica},
  author       = {Hansen, Lasse and Enevoldsen, Kenneth C. and Bernstorff, Martin and Nielbo, Kristoffer L. and Danielsen, Andreas A. and Østergaard, Søren D.},
  urldate      = {2021-09-23},
  date         = {2021},
  langid       = {english},
  note         = {Publisher: Cambridge University Press},
  keywords     = {precision medicine, mental disorders, natural language processing, machine learning, deep learning},
  file         = {Hansen et al_undefineded_The PSYchiatric clinical outcome prediction (PSYCOP) cohort.pdf:/Users/au554730/Library/Mobile Documents/com~apple~CloudDocs/zotero_storage/Hansen et al_undefineded_The PSYchiatric clinical outcome prediction (PSYCOP) cohort.pdf:application/pdf}
}

@inproceedings{chen_xgboost_2016,
  author    = {Chen, Tianqi and Guestrin, Carlos},
  title     = {{XGBoost}: A Scalable Tree Boosting System},
  booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  series    = {KDD '16},
  year      = {2016},
  isbn      = {978-1-4503-4232-2},
  location  = {San Francisco, California, USA},
  pages     = {785--794},
  numpages  = {10},
  url       = {http://doi.acm.org/10.1145/2939672.2939785},
  doi       = {10.1145/2939672.2939785},
  acmid     = {2939785},
  publisher = {ACM},
  address   = {New York, NY, USA},
  keywords  = {large-scale machine learning}
}

@article{lauritsen_framing_2021,
  title        = {The Framing of machine learning risk prediction models illustrated by evaluation of sepsis in general wards},
  volume       = {4},
  rights       = {2021 The Author(s)},
  issn         = {2398-6352},
  url          = {https://www.nature.com/articles/s41746-021-00529-x},
  doi          = {10.1038/s41746-021-00529-x},
  abstract     = {Problem framing is critical to developing risk prediction models because all subsequent development work and evaluation takes place within the context of how a problem has been framed and explicit documentation of framing choices makes it easier to compare evaluation metrics between published studies. In this work, we introduce the basic concepts of framing, including prediction windows, observation windows, window shifts and event-triggers for a prediction that strongly affects the risk of clinician fatigue caused by false positives. Building on this, we apply four different framing structures to the same generic dataset, using a sepsis risk prediction model as an example, and evaluate how framing affects model performance and learning. Our results show that an apparently good model with strong evaluation results in both discrimination and calibration is not necessarily clinically usable. Therefore, it is important to assess the results of objective evaluations within the context of more subjective evaluations of how a model is framed.},
  pages        = {1--12},
  number       = {1},
  journaltitle = {npj Digital Medicine},
  shortjournal = {npj Digit. Med.},
  author       = {Lauritsen, Simon Meyer and Thiesson, Bo and Jørgensen, Marianne Johansson and Riis, Anders Hammerich and Espelund, Ulrick Skipper and Weile, Jesper Bo and Lange, Jeppe},
  urldate      = {2022-12-07},
  date         = {2021-11-15},
  langid       = {english},
  note         = {Number: 1
                  Publisher: Nature Publishing Group},
  keywords     = {Machine learning, Epidemiology, Computer science, Biomedical engineering},
  file         = {Lauritsen et al_2021_The Framing of machine learning risk prediction models illustrated by.pdf:/Users/au554730/Library/Mobile Documents/com~apple~CloudDocs/zotero_storage/Lauritsen et al_2021_The Framing of machine learning risk prediction models illustrated by.pdf:application/pdf;Snapshot:/Users/au554730/Zotero/storage/ZCWFYXJK/s41746-021-00529-x.html:text/html}
}

@inproceedings{feurer_efficient_2015,
  title     = {Efficient and Robust Automated Machine Learning},
  volume    = {28},
  url       = {https://papers.neurips.cc/paper/2015/hash/11d0e6287202fced83f79975ec59a3a6-Abstract.html},
  abstract  = {The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning ({AutoML}) problem with the help of efficient Bayesian optimization methods. In this work we introduce a robust new {AutoML} system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub auto-sklearn, improves on existing {AutoML} methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won the first phase of the ongoing {ChaLearn} {AutoML} challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in {AutoML}. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of auto-sklearn.},
  booktitle = {Advances in Neural Information Processing Systems},
  publisher = {Curran Associates, Inc.},
  author    = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and Springenberg, Jost and Blum, Manuel and Hutter, Frank},
  urldate   = {2022-12-13},
  date      = {2015},
  file      = {Feurer et al_2015_Efficient and Robust Automated Machine Learning.pdf:/Users/au554730/Library/Mobile Documents/com~apple~CloudDocs/zotero_storage/Feurer et al_2015_Efficient and Robust Automated Machine Learning.pdf:application/pdf}
}

@article{imrie_autoprognosis_2022,
  title        = {{AutoPrognosis} 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning},
  shorttitle   = {{AutoPrognosis} 2.0},
  journaltitle = {{arXiv} preprint {arXiv}:2210.12090},
  author       = {Imrie, Fergus and Cebere, Bogdan and {McKinney}, Eoin F. and van der Schaar, Mihaela},
  date         = {2022},
  doi          = {10.48550/arXiv.2210.12090},
  file         = {Imrie et al_2022_AutoPrognosis 2.pdf:/Users/au554730/Library/Mobile Documents/com~apple~CloudDocs/zotero_storage/Imrie et al_2022_AutoPrognosis 2.pdf:application/pdf;Snapshot:/Users/au554730/Zotero/storage/NJSFPHCH/2210.html:text/html}
}

@article{shamout_machine_2021,
  title        = {Machine learning for clinical outcome prediction},
  volume       = {14},
  pages        = {116--126},
  journaltitle = {{IEEE} reviews in Biomedical Engineering},
  author       = {Shamout, Farah and Zhu, Tingting and Clifton, David A.},
  doi          = {10.1109/RBME.2020.3007816},
  date         = {2021},
  note         = {Publisher: {IEEE}}
}

@article{johnson_mimic-iii_2016,
  title        = {{MIMIC}-{III}, a freely accessible critical care database},
  volume       = {3},
  pages        = {1--9},
  number       = {1},
  journaltitle = {Scientific data},
  author       = {Johnson, Alistair {EW} and Pollard, Tom J. and Shen, Lu and Lehman, Li-wei H. and Feng, Mengling and Ghassemi, Mohammad and Moody, Benjamin and Szolovits, Peter and Anthony Celi, Leo and Mark, Roger G.},
  date         = {2016},
  doi          = {10.1038/sdata.2016.35},
  note         = {Publisher: Nature Publishing Group},
  file         = {Full Text:/Users/au554730/Zotero/storage/CMF5UETK/sdata201635.html:text/html}
}

@article{tang_democratizing_2020,
  title        = {Democratizing {EHR} analyses with {FIDDLE}: a flexible data-driven preprocessing pipeline for structured clinical data},
  volume       = {27},
  shorttitle   = {Democratizing {EHR} analyses with {FIDDLE}},
  pages        = {1921--1934},
  number       = {12},
  journaltitle = {Journal of the American Medical Informatics Association},
  author       = {Tang, Shengpu and Davarmanesh, Parmida and Song, Yanmeng and Koutra, Danai and Sjoding, Michael W. and Wiens, Jenna},
  date         = {2020},
  doi          = {10.1093/jamia/ocaa139},
  note         = {Publisher: Oxford University Press},
  file         = {Full Text:/Users/au554730/Zotero/storage/NNJJEAPK/5920826.html:text/html;Snapshot:/Users/au554730/Zotero/storage/D2T9ET2C/5920826.html:text/html}
}
