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The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the infectious disease. In this paper, we propose a generalizable machine learning approach that improves on previous efforts by recognizing a large number of clinical risk factors and SDoH. The novelty of the proposed method lies in the subtle combination of a number of deep neural networks, including the BiLSTM-CNN-CRF method and a transformer-based embedding layer. Experimental results on a cohort of COVID-19 data prepared from PubMed articles show the superiority of the proposed approach. When compared to other methods, the proposed approach achieves a performance gain of about 1–5% in terms of macro- and micro-average F1 scores. Clinical practitioners and researchers can use this approach to obtain accurate information regarding clinical risks and SDoH factors, and use this pipeline as a tool to end the pandemic or to prepare for future pandemics. [ABSTRACT FROM AUTHOR]

More than two years on, the COVID-19 pandemic continues to wreak havoc around the world and has battle-tested the pandemic-situation responses of all major global governments. Two key areas of investigation that are still unclear are: the molecular mechanisms that lead to heterogenic patient outcomes, and the causes of Post COVID condition (AKA Long-COVID). In this paper, we introduce the HYGIEIA project, designed to respond to the enormous challenges of the COVID-19 pandemic through a multi-omic approach supported by network medicine. It is hoped that in addition to investigating COVID-19, the logistics deployed within this project will be applicable to other infectious agents, pandemic-type situations, and also other complex, non-infectious diseases. Here, we first look at previous research into COVID-19 in the context of the proteome, metabolome, transcriptome, microbiome, host genome, and viral genome. We then discuss a proposed methodology for a large-scale multi-omic longitudinal study to investigate the aforementioned biological strata through high-throughput sequencing (HTS) and mass-spectrometry (MS) technologies. Lastly, we discuss how a network medicine approach can be used to analyze the data and make meaningful discoveries, with the final aim being the translation of these discoveries into the clinics to improve patient care. [ABSTRACT FROM AUTHOR]

Complex systems are inherently multilevel and multiscale systems. The infectious disease system is considered a complex system resulting from the interaction between three sub-systems (host, pathogen, and environment) organized into a hierarchical structure, ranging from the cellular to the macro-ecosystem level, with multiscales. Therefore, to describe infectious disease phenomena that change through time and space and at different scales, we built a model framework where infectious disease must be considered the set of biological responses of human hosts to pathogens, with biological pathways shared with other pathologies in an ecological interaction context. In this paper, we aimed to design a framework for building a disease model for COVID-19 based on current literature evidence. The model was set up by identifying the molecular pathophysiology related to the COVID-19 phenotypes, collecting the mechanistic knowledge scattered across scientific literature and bioinformatic databases, and integrating it using a logical/conceptual model systems biology. The model framework building process began from the results of a domain-based literature review regarding a multiomics approach to COVID-19. This evidence allowed us to define a framework of COVID-19 conceptual model and to report all concepts in a multilevel and multiscale structure. The same interdisciplinary working groups that carried out the scoping review were involved. The conclusive result is a conceptual method to design multiscale models of infectious diseases. The methodology, applied in this paper, is a set of partially ordered research and development activities that result in a COVID-19 multiscale model. [ABSTRACT FROM AUTHOR]