COVID-19 is a respiratory disease that caused a global pandemic in 2019. It is highly infectious and has the following symptoms: fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, the new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. These symptoms vary in severity; some people with many risk factors have been known to have lengthy hospital stays or die from the disease. In this paper, we analyze patients’ electronic health records (EHR) to predict the severity of their COVID-19 infection using the length of stay (LOS) as our measurement of severity. This is an imbalanced classification problem, as many people have a shorter LOS rather than a longer one. To combat this problem, we synthetically create alternate oversampled training data sets. Once we have this oversampled data, we run it through an Artificial Neural Network (ANN), which during training has its hyperparameters tuned by using bayesian optimization. We select the model with the best F1 score and then evaluate it and discuss it.

People are prone to develop opioid dependence and other health problems due to regular non-medical use, prolonged use, and misuse of opioids. The number of hospital admissions for opioid dependence is growing across the US. The length of stay (LOS) is an essential indicator that assesses the severity of opioid overdose admissions. In this paper, opioid-related healthcare data from Froedtert Health Medical System in Wisconsin are analyzed and machine learning models are proposed to predict the LOS of opioid overdose admitted patients. We also determine important features that impact the LOS. To explore the factors that significantly influence the LOS, we implemented recursive feature elimination (RFE) to select important features from the data. Since the data set is imbalanced, we applied two imbalanced learning approaches to tackle that, namely, SMOTE and imbalanced learning models. Several machine learning models were constructed and validated with 10 iterations of 10-fold cross validation, and we fine-tuned the models with the highest f1 score and AUC score. Random Forest Classifier outperforms other models trained on the oversampled data set (AUC = 0.81, precision = 0.72, recall = 0.69). Easy Ensemble Classifier, which is trained on the imbalanced data set, has a better performance in predicting longer stays (AUC = 0.80, precision = 0.70, recall = 0.74). Finally, important features identified from the predictive models can be used to determine at-risk patients of longer LOS and provide appropriate preventive care and resources.