Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.

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Attention disorders in schizophrenia are manifested in two different ways. On the one hand, the schizophrenia patient tends to keep a learned response even after it ceases to be relevant (perseveration). On the other hand, the schizophrenia patient tends to replace an adaptive response without being given a reason to do so (overswitching). In the present study, overswitching was investigated in relation to latent inhibition (LI), which is the normal ability to ignore nonrelevant stimuli. A new tool--the Combined Attention Test--was used for this purpose in a group of 41 unmedicated schizophrenia patients, divided into subgroups of patients with predominantly positive and negative symptoms, and 24 normal controls. The results show that positive schizophrenia patients, who exhibited high levels of overswitching, also revealed impaired LI, while the negative schizophrenia group, as well as normal controls, exhibited intact LI. These findings suggest that overswitching is a specific attention deficit in positive schizophrenia. We discuss the possibility that impaired LI is a consequence of overswitching and comment on the putative neurophysiology.

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