Artificial Intelligence Technologies Application for Personal Health Management

Кобринский, Б. А.; Grigoriev, Oleg; Molodchenkov, Alexey I.; Smirnov, Ivan V.; Благосклонов, Н А

doi:10.1016/j.ifacol.2019.12.448

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…e model is shown in Figure 6, where the circles represent vectors, the squares represent matrices, and the stars represent Gaussian noise. Computational Intelligence and Neuroscience e state formula of the stochastic discrete system is established, as shown in the following formula [16]:…”

Section: Health Monitoring Systemmentioning

confidence: 99%

Sports-Induced Fatigue Recovery of Competitive Aerobics Athletes Based on Health Monitoring

Wang

2022

Computational Intelligence and Neuroscience

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Exercise-induced fatigue refers to the symptom that the body cannot continue to maintain the original training volume after a certain period of continuous training. Competitive aerobics athletes have a large amount of daily training exercise, and their bodies are prone to exercise fatigue. The health monitoring technology can monitor the physical state in real time and diagnose and treat the diseases caused by the body in time. This article aims at applying the health monitoring technology to the research of sports fatigue recovery of competitive aerobics athletes. This article first briefly introduces the theoretical review of exercise-induced fatigue, and it includes the definition, classification, and determination methods of exercise-induced fatigue, and then uses the health monitoring system to describe it, and finally conducts an experiment in the diagnosis and rehabilitation of exercise-induced fatigue. The health monitoring technology was compared with traditional medical methods, and the four aspects of speed, accuracy, recovery rate, and athlete’s treatment satisfaction were tested. The experimental results show that the diagnostic accuracy based on health monitoring technology is the same as that of traditional manual medical diagnosis, reaching about 85%, which verifies its effectiveness.

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Section: Health Monitoring Systemmentioning

confidence: 99%

Sports-Induced Fatigue Recovery of Competitive Aerobics Athletes Based on Health Monitoring

Wang

2022

Computational Intelligence and Neuroscience

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“…People can also get personalized guidance from AI on how to achieve their health goals. This can be accomplished by automatically assessing a person's health and fitness data and delivering advice tailored to their specific requirements 2,3 . AI‐powered health and fitness coaching can also aid in the identification of underlying health conditions and the alerting of users to potential health dangers 4 .…”

Section: Introductionmentioning

confidence: 99%

Exploring the use of ChatGPT as a virtual health coach for chronic disease management

Al‐Anezi

2024

Learning Health Systems

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IntroductionChatGPT has been widely researched for its potential in gealthcare applications. However, its efficcy as a virtual health coach is one of the important areas, which can significantly contribute to the sustainablility in healthcare operations, especially in managing critical illnesses. Therefore, this study aims to analyze the use of ChatGPT as a virtual health coach for chronic disease managemet.MethodsThis study used a quasi‐experimental design because ChatGPT is a relatively new technology and few people have experience with it. Patients who were receiving care outside of the hospital were included. Semi‐structured interviews were conducted after a 2‐week period in which participants used ChatGPT to search for health information about chronic disease management. Thirty‐nine outpatients were interviewed and thematic analysis was used to analyze the interview data.ResultsThe findings suggested both opportunities and challenges of using ChatGPT as a virtual health coach for chronic disease management. The major opportunities identified included life‐long learning, improved health literacy, cost‐effectiveness, behavioral change support, scalability, and accessibility. The major challenges identified included limited physical examination, lack of human connection, legal and ethical complications, and lack of accuracy and reliability.ConclusionChatGPT‐based technologies may serve as a supplementary or intermediate support system. However, such applications for managing chronic diseases must protect privacy and promote both short‐ and long‐term positive outcomes.

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“…Research around personal health privacy disclosure can be broadly divided into four perspectives. The first one is from a technical perspective, which studies the development of personal health management information systems [3], the construction and development of online healthcare platforms [4], and methods of protecting medical and health privacy [5]. The second one is from the legal perspective to study the laws and regulations of health information and to consider how to reduce the privacy concerns of users [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…3 , R 3 , P 3 , B 3 ⊕ R 3 ⊕ P 3 }, the C * denotes the label category space corresponding to A, and W * −c denotes the linear layer weights corresponding to A, and b * −c is the corresponding bias value.…”

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confidence: 99%

Online Healthcare Privacy Disclosure User Group Profile Modeling Based on Multimodal Fusion

Wang

2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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With the spread of COVID-19, online healthcare is rapidly evolving to assist the public with health, reduce exposure and avoid the risk of cross-infection. Online healthcare platform requires more information from patients than offline, and insufficient or incorrect information may delay or even mislead treatment. Therefore, it is valuable to predict users’ privacy disclosure behaviors while fully protecting their information, which can provide healthcare services for users accurately and realize a personalized online healthcare environment. Compared with the traditional static online healthcare platform user privacy disclosure behavior influence factor analysis, this paper uses multimodal fusion and group profile technology to build a user privacy disclosure model and lay the foundation for personalized online healthcare services. This paper proposes a cross-modal fusion modeling approach to address the problem that the information of each modality cannot be fully utilized in the current online healthcare privacy disclosure modeling. A multimodal user profile approach is used to construct personal and group profiles, and the privacy disclosure behavioral characteristics reflected by both are integrated to realize accurate personalized services for online healthcare. The case study shows that compared with the static unimodal privacy disclosure model, the accuracy of our method gains significant improvement, which is helpful for precision healthcare services and online healthcare platform development.

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Artificial Intelligence Technologies Application for Personal Health Management

Cited by 14 publications

References 4 publications

Sports-Induced Fatigue Recovery of Competitive Aerobics Athletes Based on Health Monitoring

Sports-Induced Fatigue Recovery of Competitive Aerobics Athletes Based on Health Monitoring

Exploring the use of ChatGPT as a virtual health coach for chronic disease management

Online Healthcare Privacy Disclosure User Group Profile Modeling Based on Multimodal Fusion

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