Patient behavioral analysis is the key factor for providing treatment to patients who may suffer from various difficulties including neurological disease, head trauma, and mental disease. Analyzing the patient's behavior helps in determining the root cause of the disease. In traditional healthcare, patient behavioral analysis has lots of challenges that were much more difficult. The patient behavior can be easily analyzed with the development of smart healthcare. Information technology plays a key role in understanding the concept of smart healthcare. A new generation of information technologies including IoT and cloud computing is used for changing the traditional healthcare system in all ways. Using Internet of Things in the healthcare institution enhances the effectiveness as well as makes it more personalized and convenient to the patients. The first thing that will be discussed in the article is the technologies that have been used to support the smart class, and further, there will be a discussion on the existing problems with the smart healthcare system and how these problems can be solved. This study can provide essential information about the role of smart healthcare and IoT in maintaining behavior of patent. Various biomarkers are maintained properly with the help of these technologies. This study can provide effective information about importance of smart health system. This smart healthcare is conducted with the involvement of proper architecture. This is treated as effective energy efficiency architecture. Artificial intelligence is used increasingly in healthcare to maintain diagnosis and other important factors of healthcare. This application is also used to maintain patient engagement, which is also included in this study. Major hardware components are also included in this technology such as CO sensor and CO sensor.
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http://dx.doi.org/10.1155/2021/4028761 | DOI Listing |
J Psychosom Res
December 2024
Badminton Technical and Tactical Analysis and Diagnostic Laboratory, National Academy of Badminton, Guangzhou Sport University, Guangzhou 510500, China. Electronic address:
Purpose: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depression on cardiovascular disease (CVD) mortality among hypertensive patients. A key objective is to elucidate the interplay between mental health, lifestyle, and physical activity while comparing the effectiveness of the RSF model against the traditional Cox proportional hazards model in predicting CVD mortality.
Methods: Data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2014 were used for comprehensive depression screening.
J Adv Nurs
January 2025
School of Nursing, The Hong Kong Polytechnic University, Hong Kong, SAR, China.
Background: Sense of coherence (SoC) is a core concept of 'salutogenesis' in positive psychology, correlated with emotional distress and disease development in adults with chronic disease and older adults. A diversity of non-pharmacological interventions (NPIs) has been developed to enhance SoC, but research findings are conflicting and the adequacy of sample sizes is uncertainty.
Objective: This paper aimed to explore appropriate interventions, evaluate the effectiveness of these SoC interventions and verify the statistical robustness and reliability of pooled results.
Cureus
December 2024
Internal Medicine, Max Smart Super Speciality Hospital, New Delhi, IND.
Background Numerous risk factors have been identified for developing severe COVID-19, including sociodemographic variables and concomitant diseases. Individuals with underlying comorbidities such as diabetes, hypertension, asthma, and coronary artery disease are at a greater risk of severe illness and death. This study aimed to observe the association between risk factors and the severity of COVID-19.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electronics and Communication Engineering, Nagarjuna College of Engineering and Technology, Bengaluru, 562164, Karnataka, India.
Wireless sensor networks (WSNs) are imperative to a huge range of packages, along with environmental monitoring, healthcare structures, army surveillance, and smart infrastructure, however they're faced with numerous demanding situations that impede their functionality, including confined strength sources, routing inefficiencies, security vulnerabilities, excessive latency, and the important requirement to keep Quality of Service (QoS). Conventional strategies generally goal particular troubles, like strength optimization or improving QoS, frequently failing to provide a holistic answer that effectively balances more than one crucial elements concurrently. To deal with those challenges, we advocate a novel routing framework that is both steady and power-efficient, leveraging an Improved Type-2 Fuzzy Logic System (IT2FLS) optimized by means of the Reptile Search Algorithm (RSA).
View Article and Find Full Text PDFJMIR AI
January 2025
Human-Computer Interaction and Human-Centered AI Systems Lab, AI for Healthcare Lab, Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology, Hoboken, NJ, United States.
Background: Acute marijuana intoxication can impair motor skills and cognitive functions such as attention and information processing. However, traditional tests, like blood, urine, and saliva, fail to accurately detect acute marijuana intoxication in real time.
Objective: This study aims to explore whether integrating smartphone-based sensors with readily accessible wearable activity trackers, like Fitbit, can enhance the detection of acute marijuana intoxication in naturalistic settings.
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