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Currently submitted to: Journal of Medical Internet Research

Date Submitted: Jun 29, 2020
Open Peer Review Period: Jun 28, 2020 - Jul 7, 2020
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Clinical Characteristics and Prognostic Factors for ICU Admission of Patients with COVID-19 Using Machine Learning And Natural Language Processing

  • Jose Luis Izquierdo; 
  • Julio Ancochea; 
  • Joan B Soriano; 
  • Savana COVID-19 Research Group; 
  • Joan B Soriano; 



There remain many unknowns regarding the onset and clinical course of the ongoing COVID-19 pandemic.


Here we aimed to describe the clinical characteristics and predictors of ICU use in a large cohort of COVID-19 patients in real time.


To achieve the research objective, we used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling), to analyse the electronic health records (EHRs) of patients with COVID-19.


A total of 10,504 patients with a clinical or PCR-confirmed diagnosis of COVID-19 were identified, 52.5% males, with a mean age of 58.2 and S.D. 19.7 years. Upon admission, the most common symptoms were cough, fever, and dyspnoea, but all in less than half of cases. Overall, 6% of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm we identified that a combination of age, fever, and tachypnoea was the most parsimonious predictor of ICU admission: those younger than 56 years, without tachypnoea, and temperature <39º C, (or >39º C without respiratory crackles), were free of ICU admission. On the contrary, COVID-19 patients aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnoea and delayed their visit to the ER after being seen in primary care.


Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnoea with/without respiratory crackles) predicts which COVID-19 patients require ICU admission.


Please cite as:

Izquierdo JL, Ancochea J, Soriano JB, Savana COVID-19 Research Group , Soriano JB

Clinical Characteristics and Prognostic Factors for ICU Admission of Patients with COVID-19 Using Machine Learning And Natural Language Processing

JMIR Preprints. 29/06/2020:21801

DOI: 10.2196/preprints.21801


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