RESUMEN
Coinciding with the global pandemic of SARS-CoV-2 and the resulting global public health crisis caused by COVID-19, artificial intelligence methods started playing an ever more important role in Infectious Medicine. On one hand this was a result of a continuous digital transformation of Infectious Medicine-a trend started decades ago. On the other hand, the pandemic catalyzed the adoption of artificial intelligence and other digital and quantitative techniques by Infectious Medicine. In this chapter we review recent works touching upon aspects of COVID-19 patient journey and how it interconnects with big data and artificial intelligence. These include early and clinical research, epidemiology and detection, diagnostics, clinical care and decision support, as well as long-term care and prevention. We cross-compare the published works and assess their maturity. Finally, we provide a conclusion on the state of artificial intelligence in the Infectious Medicine of COVID-19 and attempt a future perspective. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
RESUMEN
From the outset of the COVID-19 pandemic, social media has provided a platform for sharing and discussing experiences in real time. This rich source of information may also prove useful to researchers for uncovering evolving insights into post-acute sequelae of SARS-CoV-2 (PACS), commonly referred to as Long COVID. In order to leverage social media data, we propose using entity-extraction methods for providing clinical insights prior to defining subsequent downstream tasks. In this work, we address the gap between state-of-the-art entity recognition models and the extraction of clinically relevant entities which may be useful to provide explanations for gaining relevant insights from Twitter data. We then propose an approach to bridge the gap by utilizing existing configurable tools, and datasets to enhance the capabilities of these models. Code for this work is available at: https://github.com/VectorInstitute/ProjectLongCovid-NER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
RESUMEN
From the outset of the COVID-19 pandemic, social media has provided a platform for sharing and discussing experiences in real time. This rich source of information may also prove useful to researchers for uncovering evolving insights into post-acute sequelae of SARS-CoV-2 (PACS), commonly referred to as Long COVID. In order to leverage social media data, we propose using entity-extraction methods for providing clinical insights prior to defining subsequent downstream tasks. In this work, we address the gap between state-of-the-art entity recognition models and the extraction of clinically relevant entities which may be useful to provide explanations for gaining relevant insights from Twitter data. We then propose an approach to bridge the gap by utilizing existing configurable tools, and datasets to enhance the capabilities of these models. Code for this work is available at: https://github.com/VectorInstitute/ProjectLongCovid-NER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.