ABSTRACT
The proportion of COVID-19 patients is significantly expanding around the world. Treatment with serious consideration has become a significant problem. Identifying clinical indicators of succession towards severe conditions is desperately required to empower hazard stratification and optimise resource allocation in the pandemic of COVID-19. Consequently, the classification of severity level is significant for the patient's triaging. It is required to categorise the severity level as mild, moderate, severe, and critical based on the patients' symptoms. Various symptomatic parameters may encourage the evaluation of infection seriousness. Likewise, with the rapid spread and transmissibility of COVID-19 patients, it is crucial to utilise telemonitoring schemes for COVID-19 patients. Telemonitoring mediation encourages remote data and information exchange among medicinal services, suppliers, and patients, furthermore, risk mitigation and provision of appropriate medical facilities. This paper provides explorative data analysis of symptoms, comorbidities, and other parameters, comparing different machine learning algorithms for case severity detection. This paper also provides a system (based on the degree of truthfulness) for case severity detection that might be utilised to stratify risk levels for anticipated moderate and severe COVID-19 patients. Finally, we provide a telemonitoring model of COVID-19 patients to ensure the remote and continuous monitoring of case severity progression and appropriate risk mitigation strategies. [ FROM AUTHOR] Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Background and Objective: Myofascial neck pain is a common musculoskeletal problem caused by presence of trigger points and local and referred pain patterns. Chronic neck pain is responsible for the involvement of joints, ligaments, fascia and connective tissue as well. The objective of this study was to assess the effect of Maitland mobilization in patients with myofascial chronic neck pain. Methods: In this randomized, placebo treatment-controlled trial, 30 patients consecutively aged 25-45 years meeting inclusion criteria were isolated into two groups. The study group was treated with Maitland mobilization consistently for eight weeks while the control group got placebo treatment for a similar timeframe. Visual analog Scale(VAS), Neck disability index(NDI) and cervical range of motion(ROM) questionnaire was filled by patients before, intermediate and after the intervention to evaluate the severity of pain, functional ability and range of motion. Results: Following eight weeks of treatment, when compared the post treatment effects of both groups, the significance value for VAS was 0.008, for NDI p=0.030, for Flexion p=0.573, for extension p=0.001, for right rotation p<0.001, for left rotation p=0.002, for right and left side bending p<0.001. Conclusion: The study concluded that Maitland mobilization grades(I-IV) are effective in reducing pain and improving functional level of NDI scale and the ranges of cervical spine in patients with myofascial chronic neck pain.