A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients.
Narges Razavian,Vincent J. Major,Mukund Sudarshan,Jesse Burk-Rafel,Peter Stella,Hardev Randhawa,Seda Bilaloglu,Ji Chen,Vuthy Nguy,Walter Wang,Hao Zhang,Ilan Reinstein,David Kudlowitz,Cameron Zenger,Meng Cao,Ruina Zhang,Siddhant Dogra,Keerthi B. Harish,Brian P. Bosworth,Fritz Francois,Leora I. Horwitz,Rajesh Ranganath,Jonathan S. Austrian,Yindalon Aphinyanaphongs +23 more
- Vol. 3, Iss: 1, pp 130
TLDR
A parsimonious model is developed and validated to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables, and implemented and integrated into the EHR.Abstract:
The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4-88.7] and 90.8% [90.8-90.8]) and discrimination (95.1% [95.1-95.2] and 86.8% [86.8-86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.read more
Citations
More filters
Machine learning with Python
TL;DR: This presentation is a case study taken from the travel and holiday industry and describes the effectiveness of various techniques as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib).
Posted ContentDOI
Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions
TL;DR: A survey of AI methods being used in various applications in the fight against the COVID-19 outbreak is presented and the crucial roles of AI research in this unprecedented battle are outlined.
Journal ArticleDOI
Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology
TL;DR: In this paper , a review of explainable machine learning techniques for cardiology is presented, focusing on how the nature of explanations as approximations may omit important information about how black-box models work and why they make certain predictions.
Journal ArticleDOI
Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review
TL;DR: In this paper, the authors conducted a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
Meta-Analysis: A Constantly Evolving Research Integration Tool
TL;DR: The four articles in this special section onMeta-analysis illustrate some of the complexities entailed in meta-analysis methods and contributes both to advancing this methodology and to the increasing complexities that can befuddle researchers.
Related Papers (5)
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
Laure Wynants,Laure Wynants,Ben Van Calster,Ben Van Calster,Gary S. Collins,Gary S. Collins,Richard D Riley,Georg Heinze,Ewoud Schuit,Marc J.M. Bonten,Darren Dahly,Johanna A A G Damen,Thomas P. A. Debray,Valentijn M.T. de Jong,Maarten De Vos,Paula Dhiman,Paula Dhiman,Maria C Haller,Michael O. Harhay,Liesbet Henckaerts,Pauline Heus,Michael Kammer,Nina Kreuzberger,Anna Lohmann,Kim Luijken,Jie Ma,Glen P. Martin,David J. McLernon,Constanza L Andaur Navarro,Johannes B. Reitsma,Jamie C. Sergeant,Chunhu Shi,Nicole Skoetz,Luc J.M. Smits,Kym I E Snell,Matthew Sperrin,René Spijker,René Spijker,Ewout W. Steyerberg,Toshihiko Takada,Ioanna Tzoulaki,Ioanna Tzoulaki,Sander M. J. van Kuijk,Bas C T van Bussel,Bas C T van Bussel,Iwan C. C. van der Horst,Florien S. van Royen,Jan Y Verbakel,Jan Y Verbakel,Christine Wallisch,Christine Wallisch,Jack Wilkinson,Robert Wolff,Lotty Hooft,Karel G.M. Moons,Maarten van Smeden +55 more
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.
Akhil Vaid,Sulaiman Somani,Adam Russak,Jessica K De Freitas,Fayzan Chaudhry,Ishan Paranjpe,Kipp W. Johnson,Samuel J. Lee,Riccardo Miotto,Felix Richter,Shan Zhao,Noam D. Beckmann,Nidhi Naik,Arash Kia,Prem Timsina,Anuradha Lala,Manish Paranjpe,Eddye Golden,Matteo Danieletto,Manbir Singh,Dara Meyer,Paul F. O'Reilly,Laura M. Huckins,Patricia Kovatch,Joseph Finkelstein,Robert Freeman,Edgar Argulian,Andrew Kasarskis,Bethany Percha,Judith A. Aberg,Emilia Bagiella,Carol R. Horowitz,Barbara Murphy,Eric J. Nestler,Eric E. Schadt,Judy H. Cho,Carlos Cordon-Cardo,Valentin Fuster,Dennis S. Charney,David Reich,Erwin P. Bottinger,Erwin P. Bottinger,Matthew A. Levin,Jagat Narula,Zahi A. Fayad,Allan C. Just,Alexander W. Charney,Girish N. Nadkarni,Benjamin S. Glicksberg +48 more