scholarly journals Health Status Prediction with Local-Global Heterogeneous Behavior Graph

2021 ◽  
Vol 17 (4) ◽  
pp. 1-21
Author(s):  
Xuan Ma ◽  
Xiaoshan Yang ◽  
Junyu Gao ◽  
Changsheng Xu
Keyword(s):  
Health Status ◽  
Data Streams ◽  
Health Management ◽  
Wearable Sensors ◽  
Feature Learning ◽  
Local Context ◽  
Temporal Aspects ◽  
Heterogeneous Behavior ◽  
Temporal Structures ◽  
Graph Neural Networks

Health management is getting increasing attention all over the world. However, existing health management mainly relies on hospital examination and treatment, which are complicated and untimely. The emergence of mobile devices provides the possibility to manage people’s health status in a convenient and instant way. Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. However, these data streams are multi-source and heterogeneous, containing complex temporal structures with local contextual and global temporal aspects, which makes the feature learning and data joint utilization challenging. We propose to model the behavior-related multi-source data streams with a local-global graph, which contains multiple local context sub-graphs to learn short-term local context information with heterogeneous graph neural networks and a global temporal sub-graph to learn long-term dependency with self-attention networks. Then health status is predicted based on the structure-aware representation learned from the local-global behavior graph. We take experiments on the StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.

Health Assessment Based on Fuzzy Synthetic Evaluation for Civil Aircrafts

2011 ◽  
Vol 335-336 ◽  
pp. 985-988
Author(s):  
Bao Hui Jia ◽  
Ze Dong Sun
Keyword(s):  
Health Status ◽  
Health Management ◽  
Health Assessment ◽  
Fuzzy Synthetic Evaluation ◽  
Hydraulic Pump ◽  
Quantitative Level ◽  
Key Technologies ◽  
Civil Aircraft ◽  
Evaluation Algorithm ◽  
Synthetic Evaluation

Health assessment is one of the key technologies for civil aircraft health management system. In order to access the health status of components, subsystems and systems of civil aircrafts, this paper explicitly defines the health status, and presents the fuzzy synthetic evaluation algorithm. Then the model of the evaluated object is established to get the health status of quantitative level. Finally, the method is used for health assessment of aircraft hydraulic pump .The results of simulation show the practicability of this method.

The Role of Average Health Status

2016 ◽  
pp. 884-899
Author(s):  
Jordan Panayotov
Keyword(s):  
Health Status ◽  
Population Health ◽  
Gold Standard ◽  
Health Impact ◽  
Local Context ◽  
Policy Options ◽  
The Matrix ◽  
The Right ◽  
No Gold Standard ◽  
Average Health

Economic, social and environmental policies, programs and projects have impact on health. Health in All Policies (HiAP) aims to improve population health by taking into account these impacts. HiAP needs appropriate tools for assessing impacts on population health. When making choices between policy options, decision-makers rely on predictions from Health Impact Assessment. Currently there is no gold standard for establishing and assessing validity of predictions. This paper distinguishes between two levels of causal pathways regarding health impacts – specific and conditional, and proposes the Average Health Status – Health Inequalities Matrix as gold standard. The Matrix facilitates making the right choices at any level and local context, thus is useful for researchers, policy-makers and practitioners for designing, analysing and evaluating all kinds of policies. By allowing quick, reliable and inexpensive appraisal of different policy options the matrix makes feasible taking into account the impacts on population health and paves the way for institutionalizing of HiAP.

Cloud-Based Bayesian Inference for Online People Health Status Assessment

2013 ◽  
Vol 303-306 ◽  
pp. 2231-2234
Author(s):  
Chen Shie Ho ◽  
I Po Lin
Keyword(s):  
Health Status ◽  
Bayesian Network ◽  
Domain Knowledge ◽  
Assessment Tool ◽  
Health Management ◽  
Critical Issue ◽  
Medical Decision ◽  
Physiological Data ◽  
Health Status Assessment ◽  
Status Assessment

The applications on assessment of people health status by using physiological monitoring and measurement platform have sprung up in recent years. How to design an assessment tool and provide the diagnosis result to the users by analyzing received fusion data from user side and the given domain knowledge is a critical issue in such application. This study will focus on cloud-based medical decision analysis by constructing inference model derived from incremental Bayesian network. The Bayesian network is established by physiological data retrieved from users, and the obtained report of the health status assessment can be used to facilitate user's self-health management and disease prevention.

scholarly journals What Does Big Data Mean for Wearable Sensor Systems?

2014 ◽  
Vol 23 (01) ◽  
pp. 135-142 ◽  
Author(s):  
N. H. Lovell ◽  
G. Z. Yang ◽  
A. Horsch ◽  
P. Lukowicz ◽  
L. Murrugarra ◽  
...  
Keyword(s):  
Big Data ◽  
Health Management ◽  
Wearable Sensors ◽  
Scientific Progress ◽  
Sensor Data ◽  
Sensor Systems ◽  
Wearable Sensor ◽  
Negative Consequences ◽  
Inference Problems ◽  
Care Working Group

Summary Objectives:The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. Methods: The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Health-care Working Group. Results: The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. Conclusions: Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.

scholarly journals Cultural and Health Management Practices of the Garo Community of Bangladesh: A Case Study of the Garos of Greater Mymensingh District

2009 ◽  
Vol 1 (1) ◽  
pp. 29-37
Author(s):  
Md Moklesur Rahman Sarker ◽  
Abdul Ghani
Keyword(s):  
Health Status ◽  
Management Practices ◽  
Cultural Practices ◽  
Health Management ◽  
Great Influence ◽  
Traditional Healers ◽  
Living Environment ◽  
Survey Study ◽  
The Common ◽  
Almost All

An extensive survey study was carried out on different aspects of health management practices of the Garo communities in Bangladesh to assess their actual present health status. The study was carried out on 1205 respondents out of 40,173 total Garo people of the study area of greater Mymensingh district. The study revealed that Garos enjoy a better health status than the common Bengali community. It was also observed that traditional cultural practices have great influence on the health management of the Garos. Many of the Garos think that diseases result from the dissatisfaction of the gods and goddesses or curses of the evil spirits. Thus they sacrifice animals to please the spirits to get relief of their diseases. Almost all the Garos use water from tube well (53.69%) or puller pump (44.81%) for drinking, bathing and cleaning purposes. Every family has a latrine. Almost everybody is concerned about regular dental care and half of the Garos are concerned about family planning. Garos eat a wider variety of foods including numerous natural plants as vegetables, some of which have medicinal values. Traditionally Garos are fond of drinking wine, prepared from boiled rice. The study also revealed that the Garos are generally less attacked by diseases than the common Bengali people. This may be attributed to their better living environment, food habits, cleanliness, hard work in the fields and sufficient rest after work and, after all, consciousness about health and diseases. But yet, diseases are quite common in this community; the most common one being Malaria. Most of the Garos take treatment from their traditional health practitioners although treatment of modern Allopathic system is available in the local Christian hospitals. About 55.68% of the Garos expressed their firm faith on their traditional treatment systems. In spite of some superstitions about diseases and health, the overall health status of the Garos is comparatively better than the majority of the mainland Bengali community. Key Words: Garo community, Garo culture, Health management practices, Traditional healers     doi:10.3329/sjps.v1i1.1783 S. J. Pharm. Sci. 1(1&2): 29-37

scholarly journals Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 679 ◽  
Author(s):  
Frédéric Li ◽  
Kimiaki Shirahama ◽  
Muhammad Nisar ◽  
Lukas Köping ◽  
Marcin Grzegorzek
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Wearable Sensors ◽  
Feature Learning ◽  
Human Activity Recognition ◽  
Learning Methods

scholarly journals Execution of minor patients’ right for information in professional activities of medical assistants

2015 ◽  
Vol 96 (6) ◽  
pp. 1035-1038
Author(s):  
E K Baklushina ◽  
I A Eremtsova
Keyword(s):  
Health Status ◽  
Medical Care ◽  
Health Management ◽  
Postgraduate Education ◽  
Diagnostic Procedures ◽  
Medical Assistants ◽  
Professional Activities ◽  
Patients Rights ◽  
State Medical Academy ◽  
The Right

Aim. To examine the problems of implementing patients’ rights for information by medical assistants in providing medical care of a minor under 16 years of age. Methods. The study was conducted as a poll using anonymous questionnaires and semi-standardized interviews using specially designed questionnaires and the method of expert evaluations. The study involved 407 medical assistants providing medical care for children’s population, and 427 parents of minor patients (under 15 years of age). The department of health management and public health of institute of postgraduate education of Ivanovo State Medical Academy conducted the study at the medical settings of the Vladimir and Ivanovo regions. Results. The study revealed low awareness of the medical assistants in patient’s rights for information, in particular, to have access to medical documents. Execution of the right of minor patients and their legal representatives to obtain information on the health status was shown to be inadequate, with medical assistants often ignoring the parents’ request to provide information about the health status of the child and access to medical charts and results of diagnostic procedures. A significant part of medical assistants do not consider mandatory to explain to minor patients parents the diagnostic data within their competence. Conclusion. The currents state of affairs in implementing patients’ rights for information requires development and implementation of medical and organizational measures for better awareness of medical assistants about patients’ rights, in particular, right to be informed, as well as optimizing execution of this right.

scholarly journals Wearable Sensor Application for Integrated Early Warning and Health Surveillance

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Lauren E Charles ◽  
Devin P Wright ◽  
Zhuanyi Huang ◽  
Cree White ◽  
Fnu Anubhav ◽  
...  
Keyword(s):  
Health Status ◽  
Real Time ◽  
Early Warning ◽  
Wearable Sensors ◽  
Environmental Parameters ◽  
Sensor Data ◽  
Wearable Sensor ◽  
Physiological Strain ◽  
Strain Index ◽  
Physiological Strain Index

Objective: The Wearable Sensor Application developed by Pacific Northwest National Laboratory (PNNL) provides an early warning system for stressors to individual and group health using physiologic and environmental indicators. The application integrates health monitoring parameters from wearable sensors, e.g., temperature and heart rate, with relevant environmental parameters, e.g., weather and landscape data, and calculates the corresponding physiological strain index. The information is presented to the analyst in a group and individual view with real-time alerting of abnormal health parameters. This application is the first of its kind being developed for integration into the Defense Threat Reduction Agency's Biosurveillance Ecosystem (BSVE).Introduction: Wearable devices are a low cost, minimally invasive way to monitor health. Sensor data provides real-time physiological indictors of an individual’s health status without the requirement of health care professionals or facilities. Information gleamed from wearable sensors can be used to better understand physiological stressors and prodromal symptoms. In addition, this data can be used to monitor individuals that are in high risk of health-related problems.However, raw data from wearable sensors can be overwhelming to process and laborious to monitor for an individual and, even more so, for a group of individuals. Often specific combination of ranges of sensor readings are indicative of changes to health status and need to be evaluated together or used to calculate specific signal parameters. In addition, the environment surrounding the individual needs to be considered when interpreting the data. To address these issues, PNNL has developed an application that collects, analyzes, and integrates wearable sensor data with geographic landscape and weather information to provide a real-time early alert and situational awareness tool for monitoring the health of groups and individuals.Methods: The prototype application described here was a product of PNNL’s BSVE Application Development Competition. The final product that will be deployed in the BSVE is currently under development by PNNL and will vary slightly in the exact design and architecture described.Data. Wearable sensor data was collected from the Rim2Rim (R2R) Watch Study of individuals hiking the Grand Canyon in Arizona [1]. Weather information was obtained from nearby weather stations and mapping features were derived from Google Maps.Calculations. A physiological Strain Index (PSI) was calculated using core temperature estimates derived through a Kalman Filter approach and heart rate [2,3].Application. The prototype backend application development was based in Python with a MongoDB. The front-end development was built using a scalable architecture and modular approach with components in React and D3.Results: A prototype application was developed this past summer through the PNNL BSVE App Competition (Fig 1). The application was aimed at visualizing wearable sensor data from the Grand Canyon R2R hike dataset. Simulated real-time analysis was used to calculate health status of individuals hiking based on measured physiological parameters and to alert to individuals with signs of physiologic health stress. Visualization tools were incorporated to enable sensor data for individuals and the group to be viewed simultaneously along with pertinent weather, geographic, and elevation data.Many features described in the prototype application will be incorporated into the final BSVE application. The key changes will be 1) the ability to select given time periods for viewing historical data as well as the real-time data collection, 2) environmental data and map view will come from BSVE internal data sources, and 3) the alerts will provide more information and have their own page for reviewing.Conclusions: The Wearable Sensor Application developed by PNNL for integration into the BSVE provides an early warning system for individual and group health using physiologic and environmental parameters. The application highlights health status from wearable sensors and relevant environmental parameters while monitoring a calculated physiological strain index. With this tool, an analyst can easily monitor the health of individuals and groups with the aid of real-time alerting tool for early detection of abnormal health parameters.

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