ABSTRACT
A report from the World Health Organization reveals that many people lack access to good healthcare services. Primary health care is often inaccessible, not only in developing countries, but also in developed nations like the United States. The lack of sufficient primary care physicians is one of the chief factors contributing to healthcare inaccessibility. Prior research has attempted to address the issue by examining patient symptoms and transcripts through the use of machine learning algorithms, but because numerous illnesses can produce identical symptoms, these efforts have struggled to correctly diagnose and guide patients. We sought to increase the access to healthcare services by utilizing a machine learning system to guide a patient to the appropriate specialist based on the symptoms indicated in their transcripts. In this study, we developed and evaluated an algorithm-based solution that would give the public credible, data-driven, and personalized information about their symptoms, enabling patients and their doctors to make better-educated decisions based on statistics and text transcripts. To do so, we built three models: (1) a transcript model, which uses clinical transcripts to predict the appropriate medical specialist;(2) a keyword model, which uses keyword extraction to reduce noise and isolate the symptoms from the clinical transcripts, and then uses these keywords to predict the appropriate medical specialist;and (3) a COVID-19 risk detection model, which predicts the COVID-19 risk of a patient, something that has not been fully investigated in this field of research. © 2022 IEEE.
ABSTRACT
The Covid-19 pandemic has shaken up the entire human race as it has led to more than 2.9 million deaths globally, as reported by Johns Hopkins University's Covid-19 Dashboard on April 2021. This pandemic has shaken the entire world and we are still not 'out of woods' yet. Globally, 136 mn patients have been affected by this pandemic and the healthcare infrastructure is in overdrive now. Healthcare workers are our first responders in these adverse times and the irony is that this is also significantly affecting them. A question, which needs consideration is, if our healthcare workers i.e. frontline doctors and nursing staff are succumbed to this situation, then who will save the humankind. This question needs to be answered by looking from the research perspective as to why healthcare workers and care providers are most vulnerable in these pandemic situations and why there is so much mortality in that group. We have tried to understand the key reasons behind this high mortality rate by qualitative review.
ABSTRACT
Nowadays various health-related surveys use data mining and machine learning techniques for the analysis and prediction of health-related records. Current day, people are suffering from COVID-19 health issues, which cause serious health issues around the world. To predict health-related issues, classification techniques are used. Within the classification techniques, one can process a large amount of data. Previous research uses various classification techniques for data mining applications that are k-nearest neighbour, Naives Bayes, ANN, and SVM, which takes much time to execute the result. The proposed research work uses an Ordered support vector machine (O-SVM) learning algorithm with the advance in kernel-based technique. In health-related research, the health records are collected from different sources and the algorithm will identify the research-related records in the training process. The training data sets are mentioning the normal and abnormal conditions of the patients. By using the proposed classification technique, the medical images are classified by various regions to identify the defect. This paper is mainly used for COVID-19 detection and prediction using image processing and data mining techniques. The image processing techniques are used to identify the defect presented in the image. This proposed model is done by MATLAB in the adaptation of 2018a. The proposed research work provides the best result as compared to the most recent related literature. © 2022, Scibulcom Ltd.. All rights reserved.
ABSTRACT
Background: Home-based cardiovascular disease (CVD) primary prevention (HBPP) and cardiac rehabilitation (HBCR) programs which occupied a small proportion of the overall Preventive Cardiology work in the past have become mainstream during the COVID-19 pandemic. Purpose: This study aims to analyse the effectiveness of a home-based CVD prevention program implemented during the pandemic in India. Methods: A retrospective study was conducted on pre-pandemic and pandemic enrolees. Health behaviour, CVD risk factors, physical and mental component score (PCS, MCS) from SF-12 questionnaire, body mass index (BMI), 6-minute walk distance (6MWD), and clinical and biochemical parameters were assessed. A multidisciplinary team consisting of Physician, Physiotherapist, Dietician and Counselling Psychologist provided the program using tele-health platforms. Results: Of the 66 subjects (55 ± 13 years, 73% male), 17 (26%) enrolled pre-pandemic and 49 (74%) enrolled during-pandemic, 28 (42%) were HBPP and 38 (58%) were HBCR participants. Majority of the subjects (n = 51, 77%), with significantly more HBCR than HBPP participants, harboured 4 or more risk factors (p = 0.04). In the 60 (91%) program completers, BMI, 6MWD, PCS and MCS had improved significantly. SBP, DBP, LVEF, HbA1c, total cholesterol and LDL had improved significantly in affected subjects. Completely home-based participants (n = 44, 67%) who never had any in-person contact with the team during the program also showed significant improvement. No adverse events were reported. Conclusion: Comprehensive home-based CVD prevention programs are effective in improving anthropometric, clinical, biochemical and psychosocial parameters, are a safe alternative to conventional programs and could potentially become the standard-of-care in the post-pandemic era. (Figure Presented).