ABSTRACT
The Covid-19 pandemic disturbed the smooth functioning of healthcare services throughout the world. New practices such as masking, social distancing and so on were followed to prevent the spread. Further, the severity of the problem increases for the elderly people and people having co-morbidities as proper medical care was not possible and as a result many deaths were recorded. Even for those patients who recovered from Covid could not get proper health monitoring in the Post-Covid phase as a result many deaths and severity in health conditions were reported after the Covid recovery i.e., the Post-Covid era. Technical interventions like the Internet of Things (IoT) based remote patient monitoring using Medical Internet of Things (M-IoT) wearables is one of the solutions that could help in the Post-Covid scenarios. The paper discusses a proposed framework where in a variety of IoT sensing devices along with ML algorithms are used for patient monitoring by utilizing aggregated data acquired from the registered Post-Covid patients. Thus, by using M-IoT along with Machine Learning (ML) approaches could help us in monitoring Post-Covid patients with co-morbidities for and immediate medical help. © 2023 SCPE.
ABSTRACT
The healthcare services across the world have been badly affected by the pandemic since December 2019. People have suffered in terms of medical supplies and treatments because existing medical infrastructure has failed to accommodate huge number of COVID infected patients. Further, patients with existing morbidities have been the worst hit so far and need attention. Therefore, there is a need of post-COVID care for such patients which can be achieved by using technologies such as Internet of Things (IoT) and data analytics. This paper presents medical IoT-based data analysis for post-COVID care. This paper, further, presents post-COVID data analysis to get an insight into the various symptoms across the different perspectives. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The COVID-19 has changed the scenario of patient care in most of the hospitals and healthcare centers throughout the world. Since pandemic is spread through contact with the COVID infected person, the most vulnerable community is doctors and healthcare workers. To avoid the mixing of COVID patients from other diseases, there is a need to be a separation, which will contain the spread of virus. As such, remote patient monitoring becomes very essential to take care of the patients under such situations. Because of this medical data is growing exponentially and needs to be analyzed continuously to improve the health care. Data analytics improved the performance of health care organization by proper decision making, accurate and timely information. Medical data can be explored from the sensors or medical equipments installed in the hospitals or can be collected from the fast-growing Internet of Things (IoT) devices. Visualizing the data and correlating the same with the patient monitoring for better treatment and health care is an essential part of it. This paper discusses various Machine Learning (ML) approaches for remote patient monitoring using medical IoT data for the Post-COVID patient care. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.