Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission.

Aim This study aimed to develop a predictive model to predict patients' mortality with coronavirus disease 2019 (COVID-19) from the basic medical data on the first day of admission. Methods The medical data including the demographic, clinical, and laboratory features on the first day of admission of clinically diagnosed COVID-19 patients were documented. The outcome of patients was also recorded as discharge or death.

The Role of Machine Learning and Radiomics for Treatment Response Prediction in Idiopathic Normal Pressure Hydrocephalus.

Introduction Ventricular shunting remains the standard of care for patients with idiopathic normal pressure hydrocephalus (iNPH); however, not all patients benefit from the shunting. Prediction of response in advance can result in improved patient selection for ventricular shunting. This study aims to develop a machine learning predictive model for treatment response after shunt placement using the clinical and radiomics features.