Predicting ICU Admissions based on Preconditions and Comorbidity for SARS COV2 Infected Patients

Hospitals across the globe have severe constraints in regard to ICU facilities, beds, and other life support systems. However, in certain situations including natural calamities, epidemics or pandemics, large-scale accidents, and so on, the requirement for ICU beds and resources immediately gets augmented. During such times, there exists an impending need for an optimum apportioning of ICU admissions and resources so that those patients who need critical care are given at the right point of time. The onslaught of COVID-19 pandemic has exuded a high probability of virus transmissions and subsequent complications in patients with co-morbidities and relevant medical issues, resulting in the exploration and investigation of models that could forecast the need for ICU admissions with a higher degree of accuracy. In this research study, a patient's pre-condition dataset will be used that is categorical in nature. Feature selection and extractions are implemented and the modified descriptors are provided as input to the model, for evaluating them based on the metrics namely F1-score, accuracy, specificity, and sensitivity. The prime objective is to build a predictive algorithm that will predict prior to the necessity of ICU admissions based on the patient's comorbidity/ precondition specifically for SARS COV2 infection. © 2022 IEEE.

Severity detection of COVID-19 infection with machine learning of clinical records and CT images.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment. OBJECTIVE: This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features. METHOD: P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data. RESULTS: The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers. CONCLUSION: This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.

Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan.

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e., the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time.