Big data and the goal of personalized health interventions

2022 ◽  
pp. 41-61
Author(s):  
Guy Hindley ◽  
Olav B. Smeland ◽  
Oleksandr Frei ◽  
Ole A. Andreassen
Keyword(s):  
Big Data ◽  
Health Interventions ◽  
Personalized Health

scholarly journals Research on the Secure Medical Big Data Ecosystem Based on Hadoop

2020 ◽  
Author(s):  
Xiangfeng Zhang ◽  
Yanmei Wang
Keyword(s):  
Information System ◽  
Big Data ◽  
Health Information System ◽  
Health Data ◽  
Personal Health ◽  
Medical Institutions ◽  
Data Platform ◽  
Data Ecosystem ◽  
Medical Big Data ◽  
Personalized Health

Abstract This paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. Since traditional healing activities take place in medical institutions, patient users must travel to these institutions to learn about their treatment status. The personalized health information system designed for this purpose enables patient users to understand their treatment and rehabilitation status anytime and anywhere. The above is a consideration from the perspective of the patient user, from the perspective of personal health data. Because traditional medical health data is scattered throughout different independent medical institutions, and these databases are heterogeneous. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. The characteristics of blockchain without a central server make the system without a single point In case of failure, the stability of the system is well maintained. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

scholarly journals Research on intelligent medical big data system based on Hadoop and blockchain

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiangfeng Zhang ◽  
Yanmei Wang
Keyword(s):  
Information System ◽  
Big Data ◽  
Health Information ◽  
Health Information System ◽  
Health Data ◽  
Medical Rehabilitation ◽  
Data Platform ◽  
Data Ecosystem ◽  
Medical Big Data ◽  
Personalized Health

AbstractIn order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data are stored and analyzed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

scholarly journals Research on intelligent medical big data system based on Hadoop and blockchain

2020 ◽  
Author(s):  
Xiangfeng Zhang ◽  
Yanmei Wang
Keyword(s):  
Information System ◽  
Big Data ◽  
Health Information ◽  
Health Information System ◽  
Health Data ◽  
Medical Rehabilitation ◽  
Data Platform ◽  
Data Ecosystem ◽  
Medical Big Data ◽  
Personalized Health

Abstract In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data Distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

scholarly journals Experimental Phenomenology as an Approach to the Study of Contemplative Practices

2022 ◽  
Vol 12 ◽  
Author(s):  
Lars-Gunnar Lundh
Keyword(s):  
Phenomenological Approach ◽  
Research Field ◽  
Health Interventions ◽  
Contemplative Practices ◽  
Experimental Phenomenology ◽  
Verbal Instructions ◽  
The World ◽  
Systematic Development ◽  
Experiential Effects ◽  
Personalized Health

During history humans have developed a large variety of contemplative practices, in many different areas of life, and as part of many different traditions and contexts. Although some contemplative practices are very old, the research field of Contemplation Studies is young, and there are no agreed-upon definitions of central concepts such as contemplative practices and contemplative experiences. The present paper focuses on contemplative practices, defined as practices that are engaged in for the sake of the contemplative experiences they afford (e.g., the contemplation of nature, or the contemplation of various aspects of being-in-the world). The purpose of the present paper is to discuss the potential of experimental phenomenology to contribute to the development of the research field of Contemplation Studies. Experimental phenomenology is defined as the investigation of phenomenological practices and their effects on experience. Phenomenological practices involve intentional variations of experiencing by means of changes in the direction of attention and the choice of attitude, typically as guided by verbal instructions or self-instructions. It is suggested that contemplative practices represent a subcategory of phenomenological practices. Two different varieties of experimental phenomenology are described and illustrated in the present paper: (1) an informal variety which involves the development of new phenomenological practices by creative variation of procedures and observation of effects; and (2) a more rigorously scientific variety, which involves the systematic variation of phenomenological practices in accordance with experimental designs to study their experiential effects. It is suggested that the development of contemplative practices during the ages is the result of an informal experimenting of the first kind; this variety of experimental phenomenology can also be used to develop personalized health interventions in a clinical setting. As to the more rigorously scientific experimental phenomenology, it is possible that it may lead not only to an improved understanding of general principles underlying contemplative practices, but also to a more systematic development of new contemplative practices. The experimental-phenomenological approach to contemplative practices is illustrated by various examples involving mindfulness, gratitude, receiving and giving.

Natural Scene Nutrition Information Acquisition and Analysis Based on Deep Learning

2020 ◽  
Vol 15 (7) ◽  
pp. 662-670
Author(s):  
Tianyue Zhang ◽  
Xu Wei ◽  
Zhi Li ◽  
Fangzhe Shi ◽  
Zhiqiang Xia ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Deep Learning ◽  
Information Acquisition ◽  
Recognition Rate ◽  
Natural Scene ◽  
Model Based ◽  
Connected Text ◽  
Scene Information ◽  
Personalized Health

Background: In the field of personalized health, it is often difficult for individuals to obtain professional knowledge to solve their practical problems timely and accurately. While there are some applications that can get targeted information, they often fail to function properly in nonideal environments, and they cannot achieve precise answers to individual users. Therefore, how to establish an information capture model based on big data and combine it with intelligent search is an important issue in the field of personalized health. Objective: This paper starts with the information acquisition and intelligent recommendation in the field of personalized health, and proposes a natural scene information acquisition and analysis model based on deep learning, focusing on improving the recognition rate of text in natural scenes and achieving targeted smart search to allow users to get more accurate personalized health advice. Methods: In this model, natural scene information is processed from four aspects: targeted big data collection and search, connected text proposal network text detection algorithm and projectionbased text segmentation, capsule network text recognition and result analysis. The model reduces recognition bias due to problems such as special filming conditions and photographic techniques by using deep learning algorithms. At the same time, the data mining has also improved the pertinence of the results analysis. Conclusion: This model combines deep learning and data mining methods to obtain intelligent solutions at a professional level by uploading target information images in non-ideal environments, and is suitable for accurate analysis of problems in personalized health area. Results: The proposed model is applied to analyze the user's nutrient intake requirements. The results show that the method achieves 83% prediction accuracy on the nutrient composition table dataset, and its performance is better than current convolutional neural network applications. And the model can get accurate personalized data to provide users with dietary advice.

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