Telehealth utilization during the Covid-19 pandemic: A systematic review.

During the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease. Telehealth has been widely utilised because of its usability and safety in providing healthcare services during the COVID-19 pandemic. However, a systematic literature review which provides extensive evidence on the impact of COVID-19 through telehealth and which covers multiple directions in a large-scale research remains lacking. This study aims to review telehealth literature comprehensively since the pandemic started. It also aims to map the research landscape into a coherent taxonomy and characterise this emerging field in terms of motivations, open challenges and recommendations. Articles related to telehealth during the COVID-19 pandemic were systematically searched in the WOS, IEEE, Science Direct, Springer and Scopus databases. The final set included (n = 86) articles discussing telehealth applications with respect to (i) control (n = 25), (ii) technology (n = 14) and (iii) medical procedure (n = 47). Since the beginning of the pandemic, telehealth has been presented in diverse cases. However, it still warrants further attention. Regardless of category, the articles focused on the challenges which hinder the maximisation of telehealth in such times and how to address them. With the rapid increase in the utilization of telehealth in different specialised hospitals and clinics, a potential framework which reflects the authors' implications of the future application and opportunities of telehealth has been established. This article improves our understanding and reveals the full potential of telehealth during these difficult times and beyond.

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy-TOPSIS methods.

CONTEXT AND BACKGROUND: Corona virus (COVID) has rapidly gained a foothold and caused a global pandemic. Particularists try their best to tackle this global crisis. New challenges outlined from various medical perspectives may require a novel design solution. Asymptomatic COVID-19 carriers show different health conditions and no symptoms; hence, a differentiation process is required to avert the risk of chronic virus carriers. OBJECTIVES: Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers. METHODS: The methodology includes four phases. Firstly, eight important laboratory criteria are chosen using two feature selection approaches. Real and simulation datasets from various medical perspectives are integrated to produce a new dataset involving 56 patients with different health conditions and can be used to check asymptomatic cases that can be detected within the prioritisation configuration. The first phase aims to develop a new decision matrix depending on the intersection between 'multi-laboratory criteria' and 'COVID-19 patient list'. In the second phase, entropy is utilised to set the objective weight, and TOPSIS is adapted to prioritise patients in the third phase. Finally, objective validation is performed. RESULTS: The patients are prioritised based on the selected criteria in descending order of health situation starting from the worst to the best. The proposed framework can discriminate among mild, serious and critical conditions and put patients in a queue while considering asymptomatic carriers. Validation findings revealed that the patients are classified into four equal groups and showed significant differences in their scores, indicating the validity of ranking. CONCLUSIONS: This study implies and discusses the numerous benefits of the suggested framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.

Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods

Coronavirus disease (COVID-19) pandemic has a tremendous effect on people's lives worldwide, and the number of infected patients increases daily. The healthcare sector is affected by a large number of patients with COVID-19, and a solution is urgently needed to avert the risk of deteriorating patients in terms of prioritizing patients based on their health conditions. Prioritization of patients with COVID-19 is a complex and multi-criteria decision-analysis (MCDA) problem due to (i) multiple biological laboratory examination criteria, (ii) criteria importance and (iii) trade-off amongst the criteria. This study presents a new multi-biological laboratory examination framework for prioritizing patients with COVID-19 on the basis of integrated MCDA methods. The experiment was conducted on the basis of three phases. In the first phase, patient datasets containing eight biological laboratory examination criteria for six patients with COVID-19 were derived and discussed. The outcome of this phase was used to propose a decision matrix on the basis of the intersection between "biological laboratory examination criteria" and "COVID-19 patients list". In the second phase, the analytic hierarchy process (AHP) method was utilized to set the subjective weights for the biological laboratory examination criteria by respiratory experts. In the last phase, the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method was adopted to prioritize patients in the context of individual and group decision making (GDM). Results showed that (1) the integration of AHP-VIKOR method based on individual and GDM contexts was effective for solving prioritization problems for patients with COVID-19, and (2) the prioritization results of patients with COVID-19 showed no variation in the internal and external VIKOR GDM contexts. The proposed multi-biological laboratory examination framework can differentiate between the mild and serious or critical condition of patients with COVID-19 by prioritizing them based on integrated AHP-VIKOR methods. In conclusion, medical sectors can use the proposed framework to differentiate the health conditions of infected patients and to assign appropriate care with prompt and effective treatment.

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.