A Data Capturing Platform in the Cloud for Behavioral Analysis among Smokers: An Application Platform for Public Health Research

© 2015 IEEE. Technology in the Cloud frameworks for healthcare data management and analytics has opened new horizons for public health research. Smoking is an addictive behavior and increases risk of death from different diseases, such as heart attacks or lung cancer. Nowadays, electronic cigarettes (e-cigarettes) are becoming popular in western countries and it has been recommended as an effective tool for smoking abstinence. However, smoking behavior and the efficacy of e-cigarette applications are insufficiently studied. This work presents a novel, Cloud-based infrastructure for data collection and storage to capture smoking behavior with e-cigarette. A user's smoking data generated by the daily use of e-cigarettes is uploaded to the cloud through mobile internet and a Bluetooth connection between a smart phone and the e-cigarette. All personal identity can be encrypted and a study identity number will be assigned to each subject for data privacy protection. The remote platform in the cloud can provide efficient analytic performance on a huge volume of data with high velocity of data creation. Data mining on smoking behavior will help to better understand the ways of using the e-cigarette. This data infrastructure will also be potentially used in other epidemiological studies in public health.Link_to_subscribed_fulltex

A Real-Time Decision Support Tool for Disaster Response: A Mathematical Programming Approach

© 2015 IEEE. Disasters are sudden and calamitous events that can cause severe and pervasive negative impacts on society and huge human losses. Governments and humanitarian organizations have been putting tremendous efforts to avoid and reduce the negative consequences due to disasters. In recent years, information technology and big data have played an important role in disaster management. While there has been much work on disaster information extraction and dissemination, real-Time optimization for decision support for disaster response is rarely addressed in big data research. In this paper, we propose a mathematical programming approach, with real-Time disaster-related information, to optimize the post-disaster decisions for emergency supplies delivery. This decision support tool can provide rapid and effective solutions, which are essential for disaster response.Link_to_subscribed_fulltex

Indoor Air Monitoring Platform and Personal Health Reporting System: Big Data Analytics for Public Health Research

© 2015 IEEE. Air pollution poses an increased risk for respiratory infections and lung cancer. Monitoring systems on air pollution are common for outdoor environment. In this study, our focus is on the air monitoring in household environment and connects it to a personal health reporting system through a mobile APP. Data will be captured and stored in the cloud so as to improve computational efficiency and enhance data storage capacity. Pollution data can be captured hourly year round, hence a sizeable data storage is needed in the cloud. Health statuses can be uploaded through a self-reporting system, so the data can supply useful information for other healthcare studies, and related urban planning in the future. Furthermore, data analytics based on pollution data can help identify highly polluted areas at different time points. These data are useful for the development of alert systems that can remind individuals to take personal precautions to avoid inhaling pollutants. Such alert systems are applicable to households, commercial buildings and public areas. Accumulated data on this cloud platform can support data mining in search of connections between air pollution and health outcomes, which can fuel research studies in the field of public health.Link_to_subscribed_fulltex

Embracing Big Data for Simulation Modelling of Emergency Department Processes and Activities

© 2015 IEEE. Simulation has been demonstrated to be a powerful tool to mimic processes and activities in emergency departments. However, most applications only rely on the data that were manually input by the staff in the departments. First, this practice does not guarantee that the required data to build the simulation models are captured in the computer system, as some information about the processes of emergency departments are not electronically stored. Second, human errors and missing data are also common for manual inputs. A simulation model that is incapable of representing the actual system of the emergency department will deliver wrong conclusions to hospital administrators and may lead to negative consequences if they trust the simulation results. In this paper, we present a case study of developing a simulation model of an emergency department in Hong Kong and discuss the data challenges. Then we propose an RFID-enabled infrastructure to automatically capture large volumes of data regarding the patient activities in the ED in order to build simulation models of more details and a higher accuracy.Link_to_subscribed_fulltex

Blood Pressure Management with Data Capturing in the Cloud among Hypertensive Patients: A Monitoring Platform for Hypertensive Patients

© 2015 IEEE. Hypertension is a significant modifiable risk factor for cardiovascular and kidney disease, and blood pressure (BP) control is a very important step for cardiovascular risk management. Recently, home telemonitoring BP has been suggested as an effective tool for BP control and been commonly used in Western countries. Application of technology for healthcare management becomes a trend. Health data is usually longitudinal and voluminous, an effective data management would improve the quality of healthcare service. In order to deal with the volume, variety and velocity of medical data, cloud technology has opened a new horizon, especially data for medical research. Boosting the current home telemonitoring BP system with an automatic data capturing cloud technology along with healthcare provider alert function would be a pioneer. In this study, a cloud-connected personal-based BP meter will be transformed to a research-based BP data capturing cloud platform and will observe daily use of BP measurement and upload data to the cloud through a USB hub and internet-connected personal computer. All personal identity can be decoded and a study identity number will be assigned to each user for data privacy protection. The cloud platform enables easy access for different parties from anywhere, high speed performance, strong infrastructure support and vigorous data analysis power.Link_to_subscribed_fulltex

Blood pressure monitoring on the cloud system in elderly community centres: A data capturing platform for application research in public health

© 2016 IEEE. Technology on the cloud frameworks in healthcare data management and analytics has opened new horizons for public health research. Hypertension is a significant modifiable risk factor for cardiovascular diseases. Nowadays, telemonitoring blood pressure (BP) has been suggested as an effective tool for BP control. However, elderly people always have difficulties when using electronic health monitoring devices at home. BP data capturing with cloud technology in elderly community centres under guidance and with healthcare provider alert function is a pioneer. In this study, the infrastructure of data collection is constructed on the cloud to capture behavioral data on BP meter use and BP readings. BP data will be generated by the daily use of BP measurement and uploaded to the cloud. All personal characteristics, electronic health records, BP data and call log with nurse can be encrypted and store on the cloud. The remote platform on the cloud can provide efficient analytic performance on huge volume of data with high velocity of data creation in a population-based study. Data mining on the BP measurement will help to better understand the ways to control hypertension. This platform will also be potentially used in other epidemiological studies in public health.Link_to_subscribed_fulltex

Depression after subarachnoid hemorrhage: A systematic review

Background and Purpose: Depression is common and debilitating illness accompanying many neurological disorders including non-traumatic subarachnoid hemorrhage (SAH). The aim of this systematic review was to identify and critically appraise all published studies that have reported the frequency, severity and time course of depression after SAH, the factors associated with its development and the impact of depression on patients’ quality of life after SAH. Methods: The PubMed database was searched for studies published in English that recruited at least 40 patients (\u3e18 years old) after SAH who were also diagnosed with depression. Results: Altogether 55 studies covering 6,327 patients met study entry criteria. The frequency of depression ranged from 0% to 61.7%, with a weighted proportion of 28.1%. Depression remained common even several years after the index SAH. Depression after SAH was associated with female sex, premorbid depression, anxiety, substance use disorders or any psychiatric disorders, and coping styles. Comorbid cognitive impairment, fatigue, and physical disability also increased the risk of depression. Aneurysmal SAH and infarction may be related to depression as well. Depression reduces the quality of life and life satisfaction in patients after SAH. Conclusions: Depression is common after SAH and seems to persist. Further research is needed to clarify its time course and identify the neuroendocrine and neurochemical factors and brain circuits associated with the development of post-SAH depression. Randomized controlled treatment trials targeting SAH-related depression are warranted