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2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023 ; : 336-342, 2023.
Article in English | Scopus | ID: covidwho-20240221

ABSTRACT

Big data is a very large size of datasets which come from many different sources and are in a wide variety of forms. Due to its enormous potential, big data has gained popularity in recent years. Big data enables us to investigate and reinvent numerous fields, including the healthcare industry, education, and others. Big data specifically in the healthcare sector comes from a variety of sources, including patient medical information, hospital records, findings from physical exams, and the outcomes of medical devices. Covid19 recently, one of the most neglected areas to concentrate on has come under scrutiny due to the pandemic: healthcare management. Patient duration of stay in a hospital is one crucial statistic to monitor and forecast if one wishes to increase the effectiveness of healthcare management in a hospital, even if there are many use cases for data science in healthcare management. At the time of admission, this metric aids hospitals in identifying patients who are at high Length of Stay namely LS risk (patients who will stay longer). Once identified, patients at high risk for LS can have their treatment plans improved to reduce LS and reduce the risk of infection in staff or visitors. Additionally, prior awareness of LS might help with planning logistics like room and bed allotment. The aim of the suggested system is to precisely anticipate the length of stay for each patient on an individual basis so that hospitals can use this knowledge for better functioning and resource allocation using data analytics. This would contribute to improving treatments and services. © 2023 IEEE.

2.
Annals of the Romanian Society for Cell Biology ; 25(4):9995-10002, 2021.
Article in English | Scopus | ID: covidwho-1224469

ABSTRACT

Covid-19 proceeds to have disastrous impacts on the lives of human creatures all through the world. To combat this infection, it is essential to screen the influenced patients in a quick and reasonable way. One of the most viable steps towards accomplishing this objective is through radiological examination, Chest X-Ray being the foremost easily available and slightest costly alternative. In this paper, novel logistic regression classifiers are the main progressive algorithm with the detection over Gaussian feature point training and testing. This may be valuable in an inpatient setting where the display frameworks are battling to choose whether to keep the patient in the ward beside other patients or confine them in COVID-19 zones. A three level of the identification is made as the X-Ray image classified as: Normal patient, pneumonia patient and corona patient with less time consumption. Further, we propose the utilize of cutting-edge AI methods to identify the COVID-19 patients utilizing X-Ray pictures in a mechanized way, especially in settings where radiologists are not accessible, and offer assistance make the proposed testing innovation versatile. Our solution gave a classification precision of 98.72% and affectability of 99% within the test set-up. This implementation has created a GUI application for open utilize. This application can be utilized on any computer by any restorative staff to detect COVID 19 patients utilizing Chest X-Ray pictures inside an awfully few second. © 2021, Annals of R.S.C.B. All rights reserved.

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