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## Health information technology and digital innovation for national learning health and care systems.

#### In The Lancet. Digital health Health information technology can support the development of national learning health and care systems, which can be defined as health and care systems that continuously use data-enabled infrastructure to support policy and planning, public health, and personalisation of care. The COVID-19 pandemic has offered an opportunity to assess how well equipped the UK is to leverage health information technology and apply the principles of a national learning health and care system in response to a major public health shock. With the experience acquired during the pandemic, each country within the UK should now re-evaluate their digital health and care strategies. After leaving the EU, UK countries now need to decide to what extent they wish to engage with European efforts to promote interoperability between electronic health records. Major priorities for strengthening health information technology in the UK include achieving the optimal balance between top-down and bottom-up implementation, improving usability and interoperability, developing capacity for handling, processing, and analysing data, addressing privacy and security concerns, and encouraging digital inclusivity. Current and future opportunities include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public-private partnerships. Many ethical challenges and unintended consequences of implementation of health information technology exist. To address these, there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public-private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.Sheikh Aziz, Anderson Michael, Albala Sarah, Casadei Barbara, Franklin Bryony Dean, Richards Mike, Taylor David, Tibble Holly, Mossialos Elias2021-May-06

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## Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning.

#### In Surgical innovation Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, including those with COVID-19. In light of the challenges faced with diagnostic and prognostic indicators in SARS-CoV-2 infection, our center has developed an international clinical protocol to collect standardized thoracic point of care ultrasound data in these patients for later AI/ML modeling. We surmise that in the future AI/ML may assist in the management of SARS-CoV-2 patients potentially leading to improved outcomes, and to that end, a corpus of curated ultrasound images and linked patient clinical metadata is an invaluable research resource.Mohamed Ali Abdel-Moneim, El-Alali Emran, Weltz Adam S, Rehrig Scott T2021-May-07biomedical engineering, radiologist, surgical education

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## Making a complex dental care tailored to the person: population health in focus of predictive, preventive and personalised (3P) medical approach.

#### In The EPMA journal An evident underestimation of the targeted prevention of dental diseases is strongly supported by alarming epidemiologic statistics globally. For example, epidemiologists demonstrated 100% prevalence of dental caries in the Russian population followed by clinical manifestation of periodontal diseases. Inadequately provided oral health services in populations are caused by multi-factorial deficits including but not limited to low socio-economic status of affected individuals, lack of insurance in sub-populations, insufficient density of dedicated medical units. Another important aspect is the "participatory" medicine based on the active participation of population in maintaining oral health: healthcare will remain insufficient as long as the patient is not motivated and does not feel responsible for their oral health. To this end, nearly half of chronically diseased people do not comply with adequate medical services suffering from severely progressing pathologies. Noteworthy, the prominent risk factors and comorbidities linked to the severe disease course and poor outcomes in COVID-19-infected individuals, such as elderly, diabetes mellitus, hypertension and cardiovascular disease, are frequently associated with significantly altered oral microbiome profiles, systemic inflammatory processes and poor oral health. Suggested pathomechanisms consider potential preferences in the interaction between the viral particles and the host microbiota including oral cavity, the respiratory and gastrointestinal tracts. Since an aspiration of periodontopathic bacteria induces the expression of angiotensin-converting enzyme 2, the receptor for SARS-CoV-2, and production of inflammatory cytokines in the lower respiratory tract, poor oral hygiene and periodontal disease have been proposed as leading to COVID-19 aggravation. Consequently, the issue-dedicated expert recommendations are focused on the optimal oral hygiene as being crucial for improved individual outcomes and reduced morbidity under the COVID-19 pandemic condition. Current study demonstrated that age, gender, socio-economic status, quality of environment and life-style, oral hygiene quality, regularity of dental services requested, level of motivation and responsibility for own health status and corresponding behavioural patterns are the key parameters for the patient stratification considering person-tailored approach in a complex dental care in the population. Consequently, innovative screening programmes and adapted treatment schemes are crucial for the complex person-tailored dental care to improve individual outcomes and healthcare provided to the population.Tachalov V V, Orekhova L Y, Kudryavtseva T V, Loboda E S, Pachkoriia M G, Berezkina I V, Golubnitschaja O2021-Apr-19Age, Aggravation, Bacterial load, Bacterial superinfections, Big data, Bio-banking, COVID-19, Collateral pathologies, Comorbidities, Compliance, Dental diseases, Disease severity, Dry mouth syndrome, Elderly, Gut-lung axis, Health policy, Healthcare, Hygiene, Individualised patient profiling, Inflammation, Influenza, Lower respiratory tract, Machine learning, Microbiome, Morbidity, Motivation, Oral cavity, Pathomechanism, Patient stratification, Periodontitis, Periodontopathic microflora, Predictive preventive personalised medicine (PPPM / 3PM), Probiotics, Psychological aspects, Risk factors, SARS-CoV-2, Socio-economic status, Tailored care, Treatment algorithm, Viral infection

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## Multilevel Deep-Aggregated Boosted Network to Recognize COVID-19 Infection from Large-Scale Heterogeneous Radiographic Data.

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## Diagnostic accuracy estimates for COVID-19 RT-PCR and Lateral flow immunoassay tests with Bayesian latent class models.

#### In American journal of epidemiology ; h5-index 65.0 The objective was to estimate the diagnostic accuracy of real time polymerase chain reaction (RT-PCR) and lateral flow immunoassay (LFIA) tests for COVID-19, depending on the time post symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent class models (BLCMs), which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (IgG and/or IgM) assays using RT-PCR as the reference method. ${Se}_{RT- PCR}$ was 0.68 (95% probability intervals: 0.63; 0.73). ${Se}_{IgG/M}$ was 0.32 (0.23; 0.41) for the first week and increased steadily. It was 0.75 (0.67; 0.83) and 0.93 (0.88; 0.97) for the second and third week post symptom onset, respectively. Both tests had a high to absolute Sp, with higher point median estimates for ${Sp}_{RT- PCR}$ and narrower probability intervals: ${Sp}_{RT- PCR}$ was 0.99 (0.98; 1.00) and ${Sp}_{IgG/M}$ was 0.97 (0.92; 1.00), 0.98 (0.95; 1.00) and 0.98 (0.94; 1.00) for the first, second and third week post symptom onset. The diagnostic accuracy of LFIA varies with time post symptom onset. BLCMs provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and different risk profiles.Kostoulas Polychronis, Eusebi Paolo, Hartnack Sonja2021-Mar-31Bayesian latent class models, COVID-19, LFIA, RT-PCR, Sensitivity, Specificity

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## The value of AI based CT severity scoring system in triage of patients with Covid-19 pneumonia as regards oxygen requirement and place of admission.

#### In The Indian journal of radiology & imaging Context : CT scan is a quick and effective method to triage patients in the Covid-19 pandemic to prevent the heathcare facilities from getting overwhelmed.Aims : To find whether an initial HRCT chest can help triage patient by determining their oxygen requirement, place of treatment, laboratory parameters and risk of mortality and to compare 3 CT scoring systems (0-20, 0-25 and percentage of involved lung models) to find if one is a better predictor of prognosis than the other.Settings and Design : This was a prospective observational study conducted at a Tertiary care hospital in Mumbai, Patients undergoing CT scan were included by complete enumeration method.Methods and Material : Data collected included demographics, days from swab positivity to CT scan, comorbidities, place of treatment, laboratory parameters, oxygen requirement and mortality. We divided the patients into mild, moderate and severe based on 3 criteria - 20 point CT score (OS1), 25 point CT score (OS2) and opacity percentage (OP). CT scans were analysed using CT pneumonia analysis prototype software (Siemens Healthcare version 2.5.2, Erlangen, Germany).Statistical Analysis : ROC curve and Youden's index were used to determine cut off points. Multinomial logistic regression used to study the relations with oxygen requirement and place of admission. Hosmer-Lemeshow test was done to test the goodness of fit of our models.Results : A total of 740 patients were included in our study. All the 3 scoring systems showed a significant positive correlation with oxygen requirement, place of admission and death. Based on ROC analysis a score of 4 for OS1, 9 for OS2 and 12.7% for OP was determined as the cut off for oxygen requirement.Conclusions : CT severity scoring using an automated deep learning software programme is a boon for determining oxygen requirement and triage. As the score increases, the chances of requirement of higher oxygen and intubation increase. All the three scoring systems are predictive of oxygen requirement.Kohli Anirudh, Jha Tanya, Pazhayattil Amal Babu2021-JanCovid-19, HRCT chest, oxygen requirement

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## Emotions of COVID-19: A Study of Self-Reported Information and Emotions during the COVID-19 Pandemic using Artificial Intelligence.

#### In Journal of medical Internet research ; h5-index 88.0 BACKGROUND : The COVID-19 pandemic has disrupted human societies across the world. Starting with a public health emergency, followed by a significant loss of human life, and the ensuing social restrictions leading to loss of employment, lack of interactions and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental wellbeing of all individuals impacted by the pandemic.OBJECTIVE : This research aims to investigate the human emotions of the COVID-19 pandemic expressed on social media over time, using an Artificial Intelligence framework.METHODS : Our study explores emotion classifications, intensities, transitions, profiles and alignment to key themes and topics, across the four stages of the pandemic; declaration of a global health crisis, first lockdown, easing of restrictions, and the second lockdown. This study employs an artificial intelligence framework comprising of natural language processing, word embeddings, Markov models and Growing Self-Organizing Maps that are collectively used to investigate the social media conversations. The investigation was carried out using 73,000 public Twitter conversations from users in Australia from January to September 2020.RESULTS : The outcomes of this study enabled us to analyse and visualise different emotions and related concerns expressed, reflected on social media during the COVID-19 pandemic, that can be used to gain insights on citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people have expressed during the four stages of the pandemic. It was noted that starting from personal level concerns, the concerns expressed over social media has escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that 'fear' and 'sad' emotions were more prominently expressed at first, however, they transition into 'anger' and 'disgust' over time. Negative emotions except 'sad' were significantly higher (P < .05) in the second lockdown showing increased frustration. The temporal emotion analysis was conducted by modelling the emotion state changes across the four stages which demonstrated how different emotions emerge and shift over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles.CONCLUSIONS : This study showed diverse emotions and concerns expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study establishes the use of social media to discover informed insights during a time where physical communication is impossible, the outcomes also contribute towards post-pandemic recovery, understanding psychological impact via emotion changes and potentially informing healthcare decision-making. The study exploits AI and social media to enhance our understanding of human behaviours in global emergencies, leading to improved planning and policymaking for future crises.CLINICALTRIAL : Adikari Achini, Nawaratne Rashmika, De Silva Daswin, Ranasinghe Sajani, Alahakoon Oshadi, Alahakoon Damminda2021-Apr-01

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## Predicting Lyme Disease From Patients' Peripheral Blood Mononuclear Cells Profiled With RNA-Sequencing.

#### In Frontiers in immunology ; h5-index 100.0 Although widely prevalent, Lyme disease is still under-diagnosed and misunderstood. Here we followed 73 acute Lyme disease patients and uninfected controls over a period of a year. At each visit, RNA-sequencing was applied to profile patients' peripheral blood mononuclear cells in addition to extensive clinical phenotyping. Based on the projection of the RNA-seq data into lower dimensions, we observe that the cases are separated from controls, and almost all cases never return to cluster with the controls over time. Enrichment analysis of the differentially expressed genes between clusters identifies up-regulation of immune response genes. This observation is also supported by deconvolution analysis to identify the changes in cell type composition due to Lyme disease infection. Importantly, we developed several machine learning classifiers that attempt to perform various Lyme disease classifications. We show that Lyme patients can be distinguished from the controls as well as from COVID-19 patients, but classification was not successful in distinguishing those patients with early Lyme disease cases that would advance to develop post-treatment persistent symptoms.Clarke Daniel J B, Rebman Alison W, Bailey Allison, Wojciechowicz Megan L, Jenkins Sherry L, Evangelista John E, Danieletto Matteo, Fan Jinshui, Eshoo Mark W, Mosel Michael R, Robinson William, Ramadoss Nitya, Bobe Jason, Soloski Mark J, Aucott John N, Ma’ayan Avi2021Lyme disease, PBMCs, PTLDS, RNA-seq, data mining, machine learning

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