A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data

Abstract

COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.

Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques

“…Up until now, the emphasis of our analysis lay on the prediction of the features, both discrete and continuous, most relevant for triaging patients by Severity and Mortality. As a way to confirm the validity of our results for feature selection, we note that age [ 8 , 11 , 12 , 13 ], CHD [ 4 , 11 , 12 ], CRP [ 12 , 13 ], neutrophil [ 4 , 13 ] and LDH [ 7 ] were also proven to be statistically significant features to predict the Mortality outcome, while age [ 14 ], CRP and LDH [ 3 , 5 , 9 , 14 ] (among many others) were found to be statistically significant for the Severity outcome. Furthermore, in [ 15 ], a variety of results from other studies are summarized, which possess a notable overlap with the results presented above for the statistically relevant features to predict both outcomes.…”

“…Although diagnostic tests, with variable sensitivity and specificity, have been widely available since 2020, it is still problematic to predict when a new peak of infection will present itself in a population and what measures should be taken to contain the spread while furnishing appropriate medical care. For these reasons, researchers have tried to identify specific features or test results that may be reasonably used as a predictor of the Severity of respiratory distress for COVID-19 positive patients as well as their risk of death [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ] (see also [ 15 ] and reference therein).…”

C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features

“…Therefore, selecting optimal features is regarded as one of the most influential steps in ML-based prediction [ 53 , 54 , 55 , 56 ]. Recognizing the potential of feature selection, we applied the Boruta feature selection method, which has been widely applied effectively in several biological applications [ 57 , 58 , 59 ], and consequently identified optimal features. Among these, the major contribution was from AutoC (~27%), followed by CTD, DPC, QSO, CTriad, AAC, and SOCN.…”

Feature Analysis and Machine Learning Techniques to determine severity of COVID-19 infections

“…In [36], [37], and [38] machine learning approaches were used to predict the severity of cases and assignment of ICU. They used laboratory test results, clinical reports and CT images.…”