AUTHOR=Jamshidi Elham , Asgary Amirhossein , Tavakoli Nader , Zali Alireza , Setareh Soroush , Esmaily Hadi , Jamaldini Seyed Hamid , Daaee Amir , Babajani Amirhesam , Sendani Kashi Mohammad Ali , Jamshidi Masoud , Jamal Rahi Sahand , Mansouri Nahal
TITLE=Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU
JOURNAL=Frontiers in Digital Health
VOLUME=3
YEAR=2022
URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2021.681608
DOI=10.3389/fdgth.2021.681608
ISSN=2673-253X
ABSTRACT=Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.
Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission.
Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP).
Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset.
Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.