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The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support. [ABSTRACT FROM AUTHOR]

Background: Sjögren's ('SHOW‐grins') is a chronic debilitating autoimmune disease characterised by dry eyes and dry mouth, secondary to reduced exocrine function of both the lacrimal and salivary glands. The persistent, severe and serious systemic complications of Sjögren's are poorly understood and often unappreciated, resulting in significant morbidity and treatment burden. This study aimed to explore the experiences of those living with Sjögren's, specifically access to healthcare and attitude towards telemedicine. Additionally, we sought to collect information regarding the impact of the pandemic on their quality of life (QoL). Methods: One hundred and ninety‐four individuals attended an Irish Sjögren's Webinar. Attendees were invited to participate in two online surveys after the webinar. The first survey gathered information related to demographics, disease and experiences during the COVID‐19 pandemic. A combination of bespoke items and validated questionnaires (EULAR Sjögren's Syndrome Patient Reported Index [ESSPRI], COVID‐19 Impact on Quality of Life [COV19‐QoL]) was used. The second survey consisted of a shortened Telehealth Usability Questionnaire. Both were prepared in collaboration with a patient advocate. Results: Survey 1: n = 76; response rate = 39.2%. Thirty‐one respondents (41.4%) to survey 1 reported a delay of ≥5 years between the onset of symptoms and diagnosis. Dry mouth was the most common symptom experienced (76.8%, n = 63), followed by dry eye (74.4%, n = 61), fatigue (57.3%, n = 47) and joint pain (53.7%, n = 44), but a range of other symptoms were also reported. COV19‐QoL results indicated that the pandemic had a detrimental effect on participants' overall QoL (4.0 ± 1.0) and physical health (4.0 ± 0.8) in particular. COV19‐QoL and ESSPRI scores were moderately correlated (0.36, p =.002). Over 70% of respondents had a medical appointment cancelled, delayed or rescheduled (n = 60). Survey 2: n = 57; response rate = 29.4%. Those that had interacted with telemedicine reported largely positive experiences with the virtual model. Conclusion: Clinicians should be aware of the range of symptoms experienced by patients with Sjögren's beyond those of sicca (dry eye and dry mouth) and fatigue. COVID‐19 has negatively influenced the self‐reported health and well‐being of those with Sjögren's, particularly those with higher symptom scores. It is vital that optimised telemedicine models are implemented to ensure continuity in the provision of healthcare for those with chronic illness such as Sjögren's and in preparation for possible future pandemics. Patient or Public Contribution: A group of people living with Sjögren's co‐designed the structure and content of the webinar where the survey was shared. A public and patient involvement (PPI) contributor also collaborated in the selection of questionnaires used in the study, ensuring that the questions asked would best reflect the priorities of patients. They contributed to the writing of this manuscript as co‐authors. Additionally, the research team and Sjögren's patients who contributed to this work have gone on to establish Sjögren's Research Ireland, a collaboration between patient advocates, researchers and PPI facilitators. [ABSTRACT FROM AUTHOR]

Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children. [ABSTRACT FROM AUTHOR]