Individualized prediction of COVID-19 adverse outcomes with MLHO.
Reads0
Chats0
TLDR
In this article, an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes was developed, based on data from patients' past medical records (before their COVID-19 infection).Abstract:
The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients' past medical records (before their COVID-19 infection). MLHO's architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.read more
Citations
More filters
Journal ArticleDOI
Evolving phenotypes of non-hospitalized patients that indicate long COVID.
Hossein Estiri,Zachary H. Strasser,Gabriel A. Brat,Yevgeniy R. Semenov,Chirag J. Patel,Shawn N. Murphy +5 more
TL;DR: In this paper, the authors applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19.
Posted ContentDOI
Evolving Phenotypes of non-hospitalized Patients that Indicate Long Covid
Hossein Estiri,Zachary H. Strasser,Gabriel A. Brat,Yevgeniy R. Semenov,Chirag J. Patel,Shawn N. Murphy +5 more
TL;DR: In this paper, the authors applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19.
Journal ArticleDOI
Early Measurement of Blood sST2 Is a Good Predictor of Death and Poor Outcomes in Patients Admitted for COVID-19 Infection.
Marta Sánchez-Marteles,Jorge Rubio-Gracia,Natacha Peña-Fresneda,Vanesa Garcés-Horna,Borja Gracia-Tello,Luis Martínez-Lostao,Silvia Crespo-Aznarez,Juan Ignacio Pérez-Calvo,I. Giménez-López,I. Giménez-López +9 more
TL;DR: The area under the receiver operating characteristics curve (AUC) of sST2 for endpoints was 0.776 (p = 0.001) as discussed by the authors, where the AUC is defined as the difference between the mean and the standard deviation (SD) of SST2.
Journal ArticleDOI
Current malaria infection, previous malaria exposure, and clinical profiles and outcomes of COVID-19 in a setting of high malaria transmission: an exploratory cohort study in Uganda.
Jane Achan,Asadu Serwanga,Humphrey Wanzira,Tonny Kyagulanyi,Anthony Nuwa,Godfrey Magumba,Stephen Kusasira,Isaac Sewanyana,Kevin K. A. Tetteh,Chris Drakeley,Fredrick Nakwagala,Helen Aanyu,Jimmy Opigo,Prudence Hamade,Madeleine Marasciulo,Byarugaba Baterana,James K Tibenderana +16 more
TL;DR: In this article, the potential effects of SARS-CoV-2 and Plasmodium falciparum co-infection on host susceptibility and pathogenesis remain unknown.
Journal ArticleDOI
Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data.
Thomas Campbell,Melissa P Wilson,Heinrich Roder,Samantha MaWhinney,Robert W. Georgantas,Laura Maguire,Joanna Roder,Kristine M. Erlandson +7 more
TL;DR: In this paper, the authors developed models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission, including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation.
References
More filters
Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Journal ArticleDOI
Classification and Regression Trees.
Journal ArticleDOI
Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.
Fei Zhou,Ting Yu,Ronghui Du,Guohui Fan,Ying Liu,Zhibo Liu,Jie Xiang,Yeming Wang,Bin Song,Xiaoying Gu,Xiaoying Gu,Lulu Guan,Yuan Wei,Li Hui,Xudong Wu,Jiuyang Xu,Shengjin Tu,Yi Zhang,Hua Chen,Bin Cao +19 more
TL;DR: Wang et al. as discussed by the authors used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death, including older age, high SOFA score and d-dimer greater than 1 μg/mL.
Journal ArticleDOI
Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.