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Health Promotion
Published in Rupa S. Valdez, Richard J. Holden, The Patient Factor, 2021
The Health Belief Model (HBM) explains the adoption of health behavior based on six factors related to risk assessments of both the threats to prevent and the preventive (health) behavior to adopt. The six factors include: (1) perceived susceptibility; (2) perceived severity of the threats to prevent; (3) motivations; (4) perceived benefits; (5) perceived barriers to adopt the prevention behavior; and (6) the cues to action (Janz & Becker, 1984; Rosenstock, 1974). For example, one study showed that the HBM can be used to explain the influenza vaccination behavior among adults, in that the likelihood of vaccination will increase if the perceived susceptibility to disease increases (Weinstein et al., 2007). Furthermore, meta-analyses have suggested that the HBM is able to explain the adoption of short-term health behavior such as cancer screening, medical test, and exam adoption (Carpenter, 2010; Harrison et al., 1992).
Detoxification of Biomedical Waste
Published in Ram Chandra, R.C. Sobti, Microbes for Sustainable Development and Bioremediation, 2019
Bamidele Tolulope Odumosu, Tajudeen Akanji Bamidele, Olumuyiwa Samuel Alabi, Olanike Maria Buraimoh
The use of radioactive isotopes, in the diagnosis and therapy of various diseases, is on the increase, and this has eventually contributed to the increase in the effluent of radioactive wastes generated from health facilities. Some of the commonly used radioactive compounds are isotopes of iodine (I-131, I-125, I-123), which are commonly used in the diagnosis of thyroid function and treatment of hyperthyroidism; fluorine (F-18), which is a good positron-emitting radioisotope for the development of radiopharmaceuticals for positron emission tomography (PET), a powerful nuclear medicine imaging technique; and carbon (C-14), which is used as a tracer in medical test and to date organic material.
A scheme of opinion search & relevant product recommendation in social networks using stacked DenseNet121 classifier approach
Published in Automatika, 2023
Murugesan Shanmugavelu, Muthurajkumar Sannasy
These tests are primarily categorization. Particularly binary categorization where the result is always boolean, meaning that it can only be True or False. Simply because the result is True or False does not imply that the classification is 100% accurate. Almost all medical tests include a margin of error and are not always correct. One can anticipate getting very close to 100%. And the result is determined by how close it is to being true or untrue. As a result, when something is classified using binary classification, one of the four categories is selected as the output. True Positive (TP), True Negative (TN), False Positive (FP), and False Negative are listed in that order (FN)