The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.

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Effective representation of DNA sequences is one of the important tasks in the study of genome sequences. In this paper, we propose a graphical representation of DNA sequences based on nucleotide ring structure. In the proposed representation, we convert DNA sequences into 16 dinucleotides on the surface of the hexagon so that it can preserve nucleotide’s chemical property and positional information. Our approach can provide capability of efficient similarity comparison between DNA sequences and also high comparison accuracy. Furthermore, our approach satisfies uniqueness and no degeneracy of DNA sequences. In the experimental study, we use phylogeny analysis for evolutionary relationship among different species. Extensive performance study shows that the proposed method can give better performance than existing methods in comparison with the degree of similarity.

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