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Individualized prediction of COVID-19 adverse outcomes with MLHO.
Estiri, Hossein; Strasser, Zachary H; Murphy, Shawn N.
  • Estiri H; Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, 02144, USA. hestiri@mgh.harvard.edu.
  • Strasser ZH; Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA. hestiri@mgh.harvard.edu.
  • Murphy SN; Harvard Medical School, Boston, MA, 02115, USA. hestiri@mgh.harvard.edu.
Sci Rep ; 11(1): 5322, 2021 03 05.
Article in English | MEDLINE | ID: covidwho-1118817
Semantic information from SemMedBD (by NLM)
1. COVID-19 AFFECTS Readiness
Subject
COVID-19
Predicate
AFFECTS
Object
Readiness
2. COVID-19 AFFECTS Readiness
Subject
COVID-19
Predicate
AFFECTS
Object
Readiness
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.
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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Models, Statistical / Data Mining / Machine Learning / COVID-19 Subject: Models, Statistical / Data Mining / Machine Learning / COVID-19 Type of study: Diagnostic study / Etiology study / Observational study / Prognostic study / Risk factors Language: English Journal: Sci Rep Clinical aspect: Etiology / Prediction / Prognosis Year: 2021

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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Models, Statistical / Data Mining / Machine Learning / COVID-19 Subject: Models, Statistical / Data Mining / Machine Learning / COVID-19 Type of study: Diagnostic study / Etiology study / Observational study / Prognostic study / Risk factors Language: English Journal: Sci Rep Clinical aspect: Etiology / Prediction / Prognosis Year: 2021
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