Abstract: Objective: The objective of the study was to integrate two big databases from Swiss nutrition national survey (menuCH) and Swiss health national survey 2012 for data mining purposes. Each database has a demographic base data. An integrated Swiss database is built to later discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food consumption. Design: Swiss nutrition national survey (menuCH) with approx. 2000 respondents from two different surveys, one by Phone and the other by questionnaire along with Swiss health national survey 2012 with 21500 respondents were pre-processed, cleaned and finally integrated to a unique relational database. Results: The result of this study is an integrated relational database from the Swiss nutritional and health databases.
Abstract: Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks.
Abstract: The world is expected to experience growth in the number of ageing population, and this will bring about high cost of providing care for these valuable citizens. In addition, many of these live with chronic diseases that come with old age. Providing adequate care in the face of rising costs and dwindling personnel can be challenging. However, advances in technologies and emergence of the Internet of Things are providing a way to address these challenges while improving care giving. This study proposes the integration of recommendation systems into homecare to provide real-time recommendations for effective management of people receiving care at home and those living with chronic diseases. Using the simplified Training Logic Concept, stakeholders and requirements were identified. Specific requirements were gathered from people living with cancer. The solution designed has two components namely home and community, to enhance recommendations sharing for effective care giving. The community component of the design was implemented with the development of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). This component has illustrated the possibility of real-time recommendations, improved recommendations sharing among care receivers and between a physician and care receivers. Full implementation will increase access to health data for better care decision making.
Abstract: Situation Awareness can offer the potential for conscious dynamic reflection. In an era of online health data sharing, it is becoming increasingly important that users of health social networks (HSNs) have the information necessary to make informed decisions as part of the registration process and in the provision of eConsent. This research aims to leverage an adapted Situation Awareness (SA) model to explore users’ decision making processes in the provision of eConsent. A HSN platform was used to investigate these behaviours. A mixed methods approach was taken. This involved the observation of registration behaviours followed by a questionnaire and focus group/s. Early results suggest that users are apt to automatically accept eConsent, and only later consider the long-term implications of sharing their personal health information. Further steps are required to continue developing knowledge and understanding of this important eConsent process. The next step in this research will be to develop a set of guidelines for the improved presentation of eConsent on the HSN platform.
Abstract: Cloud computing can reduce the start-up expenses of implementing EHR (Electronic Health Records). However, many of the healthcare institutions are yet to implement cloud computing due to the associated privacy and security issues. In this paper, we analyze the challenges and opportunities of implementing cloud computing in healthcare. We also analyze data of over 5000 US hospitals that use Telemedicine applications. This analysis helps to understand the importance of smart phones over the desktop systems in different departments of the healthcare institutions. The wide usage of smartphones and cloud computing allows ubiquitous and affordable access to the health data by authorized persons, including patients and doctors. Cloud computing will prove to be beneficial to a majority of the departments in healthcare. Through this analysis, we attempt to understand the different healthcare departments that may benefit significantly from the implementation of cloud computing.
Abstract: Measuring the Electrocardiogram (ECG) signal is an
essential process for the diagnosis of the heart diseases. The ECG
signal has the information of the degree of how much the heart
performs its functions. In medical diagnosis and treatment systems,
Decision Support Systems processing the ECG signal are being
developed for the use of clinicians while medical examination. In this
study, a modular wireless ECG (WECG) measuring and recording
system using a single board computer and e-Health sensor platform
is developed. In this designed modular system, after the ECG signal
is taken from the body surface by the electrodes first, it is filtered and
converted to digital form. Then, it is recorded to the health database
using Wi-Fi communication technology. The real time access of the
ECG data is provided through the internet utilizing the developed
web interface.
Abstract: This paper describes the tradeoffs and the design from
scratch of a self-contained, easy-to-use health dashboard software
system that provides customizable data tracking for patients in smart
homes. The system is made up of different software modules and
comprises a front-end and a back-end component. Built with HTML,
CSS, and JavaScript, the front-end allows adding users, logging into
the system, selecting metrics, and specifying health goals. The backend
consists of a NoSQL Mongo database, a Python script, and a
SimpleHTTPServer written in Python. The database stores user
profiles and health data in JSON format. The Python script makes use
of the PyMongo driver library to query the database and displays
formatted data as a daily snapshot of user health metrics against
target goals. Any number of standard and custom metrics can be
added to the system, and corresponding health data can be fed
automatically, via sensor APIs or manually, as text or picture data
files. A real-time METAR request API permits correlating weather
data with patient health, and an advanced query system is
implemented to allow trend analysis of selected health metrics over
custom time intervals. Available on the GitHub repository system,
the project is free to use for academic purposes of learning and
experimenting, or practical purposes by building on it.
Abstract: The proliferation of health data standards today is somewhat overlapping and conflicting, resulting in market confusion and leading to increasing proprietary interests. The government role and support in standardization for health data are thought to be crucial in order to establish credible standards for the next decade, to maximize interoperability across the health sector, and to decrease the risks associated with the implementation of non-standard systems. The normative literature missed out the exploration of the different steps required to be undertaken by the government towards the development of national health data standards. Based on the lessons learned from a qualitative study investigating the different issues to the adoption of health data standards in the major tertiary hospitals in Saudi Arabia and the opinions and feedback from different experts in the areas of data exchange and standards and medical informatics in Saudi Arabia and UK, a list of steps required towards the development of national health data standards was constructed. Main steps are the existence of: a national formal reference for health data standards, an agreed national strategic direction for medical data exchange, a national medical information management plan and a national accreditation body, and more important is the change management at the national and organizational level. The outcome of this study can be used by academics and practitioners to develop the planning of health data standards, and in particular those in developing countries.
Abstract: The environment pollution with pesticides and heavy
metals is a recognized problem nowadays, with extension to the
global scale the tendency of amplification. Even with all the progress
in the environmental field, both in the emphasize of the effect of the
pollutants upon health, the linked studies environment-health are
insufficient, not only in Romania but all over the world also. We aim
to describe the particular situation in Romania regarding the
uncontrolled use of pesticides, to identify and evaluate the risk zones
for health and the environment in Romania, with the final goal of
designing adequate programs for reduction and control of the risk
sources. An exploratory study was conducted to determine the
magnitude of the pesticide use problem in a population living in
Saliste, a rural setting in Transylvania, Romania. The significant
stakeholders in Saliste region were interviewed and a sample from
the population living in Saliste area was selected to fill in a designed
questionnaire. All the selected participants declared that they used
pesticides in their activities for more than one purpose. They
declared they annually applied pesticides for a period of time
between 11 and 30 years, from 5 to 9 days per year on average,
mainly on crops situated at some distance from the houses but high
risk behavior was identified as the volunteers declared the use of
pesticides in the backyard gardens, near their homes, where children
were playing. The pesticide applicators did not have the necessary
knowledge about safety and exposure. The health data must be
correlated with exposure biomarkers in attempt to identify the
possible health effects of the pesticides exposure. Future plans
include educational campaigns to raise the awareness of the
population on the danger of uncontrolled use of pesticides.
Abstract: In ubiqutious healthcare environment, user's health data are transfered to the remote healthcare server by the user's wearable system or mobile phone. These collected user's health data should be managed and analyzed in the healthcare server, so that care giver or user can monitor user's physiological state. In this paper, we designed and developed the intelligent Healthcare Server to manage the user's health data using CDSS and ontology. Our system can analyze user's health data semantically using CDSS and ontology, and report the result of user's physiological raw data to the user and care giver.
Abstract: Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.