When there are many people who don't need to expect something more than the benefits to take, we will suggest you to have willing to reach all benefits. Be sure and surely do to take this problem based learning an approach to medical education that gives the best reasons to read. When you really need to get the reason why, this problem based learning an approach to medical education book will probably make you feel curious.

Problem-Based Learning: An Approach to Medical Education

Background: Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease
Objective: The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas This may be of great help to many health care administrators, computer scientists, and policy makers worldwide
Methods: We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19
Results: Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19 These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine
Conclusions: We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed

/pdf/approaches-based-on-artificial-intelligence-and-the-internet-1lypav1brv.pdf

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review.

The Omicron variant of the SARS-CoV-2 virus carries mutations, which enable it to evade immunity conferred by vaccines and previous infections.We used a Cox proportional hazards model and a logistic regression model on individual-level data on all laboratory-confirmed SARS-CoV-2 infections in the Czech Republic to estimate the relative risk of infection, hospitalization, including severe states, for Delta and Omicron variants, adjusting for sex, age, previous infection, vaccine type and vaccination status. A recent (<2 months) two-dose vaccination reached VE 43% (95% CI: 42-44) against infection by Omicron compared to 73% (95% CI: 72-74) against Delta. A recent booster increased VE to 56% (95% CI: 55-56) against Omicron infection compared to 90% (95% CI: 90-91) for Delta. The VE against Omicron hospitalization of a recent two-dose vaccination was 45% (95% CI: 29-57), with a recent booster 87% (95% CI: 84-88). The VE against the need for oxygen therapy due to Omicron was 57% (95% CI: 32-72) for recent vaccination, 90% (95% CI: 87-92) for a recent booster. Post-infection protection against Omicron hospitalization declined from 68% (95% CI: 68-69) at <6 months to 13% (95% CI: 11-14) at >6 months after a previous infection. A recent combination of a previous infection and vaccination was more protective then either alone with a slight benefit from a vaccination preceding an infection. Once infected, the OR for Omicron relative to Delta was 0.36 (95% CI: 0.34-0.38) for hospitalization, 0.24 (95% CI: 0.22-0.26) for oxygen therapy, and 0.24 (95% CI: 0.21-0.28) for ICU admission

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9129207

Protection by vaccines and previous infection against the Omicron variant of SARS-CoV-2

Abstract The reluctance of people to get vaccinated represents a fundamental challenge to containing the spread of deadly infectious diseases 1,2 , including COVID-19. Identifying misperceptions that can fuel vaccine hesitancy and creating effective communication strategies to overcome them are a global public health priority 3–5 . Medical doctors are a trusted source of advice about vaccinations 6 , but media reports may create an inaccurate impression that vaccine controversy is prevalent among doctors, even when a broad consensus exists 7,8 . Here we show that public misperceptions about the views of doctors on the COVID-19 vaccines are widespread, and correcting them increases vaccine uptake. We implement a survey among 9,650 doctors in the Czech Republic and find that 90% of doctors trust the vaccines. Next, we show that 90% of respondents in a nationally representative sample ( n = 2,101) underestimate doctors’ trust; the most common belief is that only 50% of doctors trust the vaccines. Finally, we integrate randomized provision of information about the true views held by doctors into a longitudinal data collection that regularly monitors vaccination status over 9 months. The treatment recalibrates beliefs and leads to a persistent increase in vaccine uptake. The approach demonstrated in this paper shows how the engagement of professional medical associations, with their unparalleled capacity to elicit individual views of doctors on a large scale, can help to create a cheap, scalable intervention that has lasting positive impacts on health behaviour. 

/pdf/communicating-doctors-consensus-persistently-increases-covid-1k462z6o.pdf

C﻿ommunicating doctors’ consensus persistently increases COVID-19 vaccinations

Background: Contact tracing is a widely adopted surveillance system that is used to identify, evaluate, and handle people who have been exposed to novel infectious diseases. The mobile phone apps using a digital technological system, called “proximity tracking,” is used as a surveillance system to control the COVID-19 pandemic.

Use of Mobile Phone Apps for Contact Tracing to Control the COVID-19 Pandemic: A Literature Review

Background: The beginning of the coronavirus disease (COVID-19) epidemic dates back to December 31, 2019, when the first cases were reported in the People’s Republic of China. In the Czech Republic, the first three cases of infection with the novel coronavirus were confirmed on March 1, 2020. The joint effort of state authorities and researchers gave rise to a unique team, which combines methodical knowledge of real-world processes with the know-how needed for effective processing, analysis, and online visualization of data.
Objective: Due to an urgent need for a tool that presents important reports based on valid data sources, a team of government experts and researchers focused on the design and development of a web app intended to provide a regularly updated overview of COVID-19 epidemiology in the Czech Republic to the general population.
Methods: The cross-industry standard process for data mining model was chosen for the complex solution of analytical processing and visualization of data that provides validated information on the COVID-19 epidemic across the Czech Republic. Great emphasis was put on the understanding and a correct implementation of all six steps (business understanding, data understanding, data preparation, modelling, evaluation, and deployment) needed in the process, including the infrastructure of a nationwide information system; the methodological setting of communication channels between all involved stakeholders; and data collection, processing, analysis, validation, and visualization.
Results: The web-based overview of the current spread of COVID-19 in the Czech Republic has been developed as an online platform providing a set of outputs in the form of tables, graphs, and maps intended for the general public. On March 12, 2020, the first version of the web portal, containing fourteen overviews divided into five topical sections, was released. The web portal’s primary objective is to publish a well-arranged visualization and clear explanation of basic information consisting of the overall numbers of performed tests, confirmed cases of COVID-19, COVID-19-related deaths, the daily and cumulative overviews of people with a positive COVID-19 case, performed tests, location and country of infection of people with a positive COVID-19 case, hospitalizations of patients with COVID-19, and distribution of personal protective equipment.
Conclusions: The online interactive overview of the current spread of COVID-19 in the Czech Republic was launched on March 11, 2020, and has immediately become the primary communication channel employed by the health care sector to present the current situation regarding the COVID-19 epidemic. This complex reporting of the COVID-19 epidemic in the Czech Republic also shows an effective way to interconnect knowledge held by various specialists, such as regional and national methodology experts (who report positive cases of the disease on a daily basis), with knowledge held by developers of central registries, analysts, developers of web apps, and leaders in the health care sector.

/pdf/complex-reporting-of-the-covid-19-epidemic-in-the-czech-39p1do1n9y.pdf

Complex Reporting of the COVID-19 Epidemic in the Czech Republic: Use of an Interactive Web-Based App in Practice.

Medical and healthcare study programmes are quite complicated in terms of branched structure and heterogeneous content. In logical sequence a lot of requirements and demands placed on students appear there. This paper focuses on an innovative way how to discover and understand complex curricula using modern information and communication technologies. We introduce an algorithm for curriculum metadata automatic processing — automatic keyword extraction based on unsupervised approaches, and we demonstrate a real application during a process of innovation and optimization of medical education. The outputs of our pilot analysis represent systematic description of medical curriculum by three different approaches (centrality measures) used for relevant keywords extraction. Further evaluation by senior curriculum designers and guarantors is required to obtain an objective benchmark.

Automatic keyword extraction from medical and healthcare curriculum

At the time of the COVID-19 pandemic, providing access to data (properly optimised regarding personal data protection) plays a crucial role in providing the general public and media with up-to-date information. Open datasets also represent one of the means for evaluation of the pandemic on a global level. The primary aim of this paper is to describe the methodological and technical framework for publishing datasets describing characteristics related to the COVID-19 epidemic in the Czech Republic (epidemiology, hospital-based care, vaccination), including the use of these datasets in practice. Practical aspects and experience with data sharing are discussed. As a reaction to the epidemic situation, a new portal COVID-19: Current Situation in the Czech Republic (https://onemocneni-aktualne.mzcr.cz/covid-19) was developed and launched in March 2020 to provide a fully-fledged and trustworthy source of information for the public and media. The portal also contains a section for the publication of (i) public open datasets available for download in CSV and JSON formats and (ii) authorised-access-only section where the authorised persons can (through an online generated token) safely visualise or download regional datasets with aggregated data at the level of the individual municipalities and regions. The data are also provided to the local open data catalogue (covering only open data on healthcare, provided by the Ministry of Health) and to the National Catalogue of Open Data (covering all open data sets, provided by various authorities/publishers, and harversting all data from local catalogues). The datasets have been published in various authentication regimes and widely used by general public, scientists, public authorities and decision-makers. The total number of API calls since its launch in March 2020 to 15 December 2020 exceeded 13 million. The datasets have been adopted as an official and guaranteed source for outputs of third parties, including public authorities, non-governmental organisations, scientists and online news portals. Datasets currently published as open data meet the 3-star open data requirements, which makes them machine-readable and facilitates their further usage without restrictions. This is essential for making the data more easily understandable and usable for data consumers. In conjunction with the strategy of the MH in the field of data opening, additional datasets meeting the already implemented standards will be also released, both on COVID-19 related and unrelated topics.

/pdf/sharing-datasets-of-the-covid-19-epidemic-in-the-czech-3k2cy05z.pdf

Sharing datasets of the COVID-19 epidemic in the Czech Republic

Curriculum design and implementation in higher medical education can be a great challenge. Although there are well-defined standards, such as the Curriculum Inventory and Competency Framework by MedBiquitous Consortium, existing systems are incapable of a visual representation of the various components, attributes, and relations. In this paper, we present the MEDCIN platform, a pilot tool which uses a standard-compliant curriculum data model to offer comprehensive and thorough analysis of a given curriculum. In addition, the ongoing research in challenging areas, such as the curriculum content comparison, can reveal valuable knowledge from existing data and transform the future of medical education.

A Pilot Medical Curriculum Analysis and Visualization According to Medbiquitous Standards

Medicai and healthcare study programmes cover various curricula consisting of many theoretically focused courses and clinical teaching training. Curriculum attributes usually contains thousands of requirements on the form of knowledge and skills which fully define a complete graduate profile. It is not humanly possible to go through the entire curriculum or to imagine how the individual courses, learning units, outcomes and branches of medicine are interrelated. This paper introduces an innovative analytical approach which helps to identify automatically the most frequent topics based on keyword extraction. Moreover, the transparent and clear web-based visualisation of achieved results is shown in practice.

/pdf/towards-a-keyword-extraction-in-medical-and-healthcare-23rcbt5eg5.pdf

Matej Karolyi

Papers

Complex Reporting of the COVID-19 Epidemic in the Czech Republic: Use of an Interactive Web-Based App in Practice.

Automatic keyword extraction from medical and healthcare curriculum

Sharing datasets of the COVID-19 epidemic in the Czech Republic

A Pilot Medical Curriculum Analysis and Visualization According to Medbiquitous Standards

Towards a keyword extraction in medical and healthcare education