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Health monitoring data mining

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https://www.readbyqxmd.com/read/28214992/data-mining-in-hiv-aids-surveillance-system-application-to-portuguese-data
#1
Alexandra Oliveira, Brígida Mónica Faria, A Rita Gaio, Luís Paulo Reis
The Human Immunodeficiency Virus (HIV) is an infectious agent that attacks the immune system cells. Without a strong immune system, the body becomes very susceptible to serious life threatening opportunistic diseases. In spite of the great progresses on medication and prevention over the last years, HIV infection continues to be a major global public health issue, having claimed more than 36 million lives over the last 35 years since the recognition of the disease. Monitoring, through registries, of HIV-AIDS cases is vital to assess general health care needs and to support long-term health-policy control planning...
April 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28174760/mining-discriminative-patterns-to-predict-health-status-for-cardiopulmonary-patients
#2
Qian Cheng, Jingbo Shang, Joshua Juen, Jiawei Han, Bruce Schatz
Smartphones are ubiquitous now, but it is still unclear what physiological functions they can monitor at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown that predictive models can accurately classify cardiopulmonary conditions from healthy status, as well as different severity levels within cardiopulmonary disease, the GOLD stages. Here we propose several universal models to monitor cardiopulmonary conditions, including DPClass, a novel learning approach we designed...
October 2016: ACM-BCB: ACM Conference on Bioinformatics, Computational Biology and Biomedicine
https://www.readbyqxmd.com/read/28162030/infodemiology-of-systemic-lupus-erythematous-using-google-trends
#3
M Radin, S Sciascia
Objective People affected by chronic rheumatic conditions, such as systemic lupus erythematosus (SLE), frequently rely on the Internet and search engines to look for terms related to their disease and its possible causes, symptoms and treatments. 'Infodemiology' and 'infoveillance' are two recent terms created to describe a new developing approach for public health, based on Big Data monitoring and data mining. In this study, we aim to investigate trends of Internet research linked to SLE and symptoms associated with the disease, applying a Big Data monitoring approach...
January 1, 2017: Lupus
https://www.readbyqxmd.com/read/28130773/identification-of-substandard-medicines-via-disproportionality-analysis-of-individual-case-safety-reports
#4
Zahra Anita Trippe, Bruno Brendani, Christoph Meier, David Lewis
INTRODUCTION: The distribution and use of substandard medicines (SSMs) is a public health concern worldwide. The detection of SSMs is currently limited to expensive large-scale assay techniques such as high-performance liquid chromatography (HPLC). Since 2013, the Pharmacovigilance Department at Novartis Pharma AG has been analyzing drug-associated adverse events related to 'product quality issues' with the aim of detecting defective medicines using spontaneous reporting. The method of identifying SSMs with spontaneous reporting was pioneered by the Monitoring Medicines project in 2011...
January 28, 2017: Drug Safety: An International Journal of Medical Toxicology and Drug Experience
https://www.readbyqxmd.com/read/28126387/research-and-application-of-a-hybrid-model-based-on-dynamic-fuzzy-synthetic-evaluation-for-establishing-air-quality-forecasting-and-early-warning-system-a-case-study-in-china
#5
Yunzhen Xu, Pei Du, Jianzhou Wang
As the atmospheric environment pollution has been becoming more and more serious in China, it is highly desirable to develop a scientific and effective early warning system that plays a great significant role in analyzing and monitoring air quality. However, establishing a robust early warning system for warning the public in advance and ameliorating air quality is not only an extremely challenging task but also a public concerned problem for human health. Most previous studies are focused on improving the prediction accuracy, which usually ignore the significance of uncertainty information and comprehensive evaluation concerning air pollutants...
January 23, 2017: Environmental Pollution
https://www.readbyqxmd.com/read/28073737/using-real-time-social-media-technologies-to-monitor-levels-of-perceived-stress-and-emotional-state-in-college-students-a-web-based-questionnaire-study
#6
Sam Liu, Miaoqi Zhu, Dong Jin Yu, Alexander Rasin, Sean D Young
BACKGROUND: College can be stressful for many freshmen as they cope with a variety of stressors. Excess stress can negatively affect both psychological and physical health. Thus, there is a need to find innovative and cost-effective strategies to help identify students experiencing high levels of stress to receive appropriate treatment. Social media use has been rapidly growing, and recent studies have reported that data from these technologies can be used for public health surveillance...
January 10, 2017: JMIR Mental Health
https://www.readbyqxmd.com/read/27895924/urinary-arsenic-species-concentration-in-residents-living-near-abandoned-metal-mines-in-south-korea
#7
Jin-Yong Chung, Byoung-Gwon Kim, Byung-Kook Lee, Jai-Dong Moon, Joon Sakong, Man Joong Jeon, Jung-Duck Park, Byung-Sun Choi, Nam-Soo Kim, Seung-Do Yu, Jung-Wook Seo, Byeong-Jin Ye, Hyoun-Ju Lim, Young-Seoub Hong
BACKGROUND: Arsenic is a carcinogenic heavy metal that has a species-dependent health effects and abandoned metal mines are a source of significant arsenic exposure. Therefore, the aims of this study were to analyze urinary arsenic species and their concentration in residents living near abandoned metal mines and to monitor the environmental health effects of abandoned metal mines in Korea. METHODS: This study was performed in 2014 to assess urinary arsenic excretion patterns of residents living near abandoned metal mines in South Korea...
2016: Annals of Occupational and Environmental Medicine
https://www.readbyqxmd.com/read/27650473/pm-10-episodes-in-greece-local-sources-versus-long-range-transport-observations-and-model-simulations
#8
Vasileios N Matthaios, Athanasios G Triantafyllou, Petros Koutrakis
: Periods of abnormally high concentrations of atmospheric pollutants, defined as air pollution episodes, can cause adverse health effects. Southern European countries experience high particulate matter (PM) levels originating from local and distant sources. In this study, we investigated the occurrence and nature of extreme PM10 (PM with an aerodynamic diameter ≤10 μm) pollution episodes in Greece. We examined PM10 concentration data from 18 monitoring stations located at five sites across the country: (1) an industrial area in northwestern Greece (Western Macedonia Lignite Area, WMLA), which includes sources such as lignite mining operations and lignite power plants that generate a high percentage of the energy in Greece; (2) the greater Athens area, the most populated area of the country; and (3) Thessaloniki, (4) Patra, and (5) Volos, three large cities in Greece...
2017: Journal of the Air & Waste Management Association
https://www.readbyqxmd.com/read/27549158/pedagogical-monitoring-as-a-tool-to-reduce-dropout-in-distance-learning-in-family-health
#9
Deborah de Castro E Lima Baesse, Alexandra Monteiro Grisolia, Ana Emilia Figueiredo de Oliveira
BACKGROUND: This paper presents the results of a study of the Monsys monitoring system, an educational support tool designed to prevent and control the dropout rate in a distance learning course in family health. Developed by UNA-SUS/UFMA, Monsys was created to enable data mining in the virtual learning environment known as Moodle. METHODS: This is an exploratory study using documentary and bibliographic research and analysis of the Monsys database. Two classes (2010 and 2011) were selected as research subjects, one with Monsys intervention and the other without...
August 22, 2016: BMC Medical Education
https://www.readbyqxmd.com/read/27517928/human-behavior-analysis-by-means-of-multimodal-context-mining
#10
Oresti Banos, Claudia Villalonga, Jaehun Bang, Taeho Hur, Donguk Kang, Sangbeom Park, Thien Huynh-The, Vui Le-Ba, Muhammad Bilal Amin, Muhammad Asif Razzaq, Wahajat Ali Khan, Choong Seon Hong, Sungyoung Lee
There is sufficient evidence proving the impact that negative lifestyle choices have on people's health and wellness. Changing unhealthy behaviours requires raising people's self-awareness and also providing healthcare experts with a thorough and continuous description of the user's conduct. Several monitoring techniques have been proposed in the past to track users' behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters...
2016: Sensors
https://www.readbyqxmd.com/read/27471222/unsupervised-detection-and-analysis-of-changes-in-everyday-physical-activity-data
#11
Gina Sprint, Diane J Cook, Maureen Schmitter-Edgecombe
Sensor-based time series data can be utilized to monitor changes in human behavior as a person makes a significant lifestyle change, such as progress toward a fitness goal. Recently, wearable sensors have increased in popularity as people aspire to be more conscientious of their physical health. Automatically detecting and tracking behavior changes from wearable sensor-collected physical activity data can provide a valuable monitoring and motivating tool. In this paper, we formalize the problem of unsupervised physical activity change detection and address the problem with our Physical Activity Change Detection (PACD) approach...
October 2016: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/27455108/applying-gis-and-machine-learning-methods-to-twitter-data-for-multiscale-surveillance-of-influenza
#12
Chris Allen, Ming-Hsiang Tsou, Anoshe Aslam, Anna Nagel, Jean-Mark Gawron
Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013-2014 flu season...
2016: PloS One
https://www.readbyqxmd.com/read/27363257/the-fitbit-fault-line-two-proposals-to-protect-health-and-fitness-data-at-work
#13
Elizabeth A Brown
Employers are collecting and using their employees' health data, mined from wearable fitness devices and health apps, in new, profitable, and barely regulated ways. The importance of protecting employee health and fitness data will grow exponentially in the future. This is the moment for a robust discussion of how law can better protect employees from the potential misuse of their health data. While scholars have just begun to examine the problem of health data privacy, this Article contributes to the academic literature in three important ways...
2016: Yale Journal of Health Policy, Law, and Ethics
https://www.readbyqxmd.com/read/27239711/coral-skeletal-geochemistry-as-a-monitor-of-inshore-water-quality
#14
REVIEW
Narottam Saha, Gregory E Webb, Jian-Xin Zhao
Coral reefs maintain extraordinary biodiversity and provide protection from tsunamis and storm surge, but inshore coral reef health is degrading in many regions due to deteriorating water quality. Deconvolving natural and anthropogenic changes to water quality is hampered by the lack of long term, dated water quality data but such records are required for forward modelling of reef health to aid their management. Reef corals provide an excellent archive of high resolution geochemical (trace element) proxies that can span hundreds of years and potentially provide records used through the Holocene...
October 1, 2016: Science of the Total Environment
https://www.readbyqxmd.com/read/27225547/multi-parametric-prediction-for-cardiovascular-risk-assessment
#15
Jorge Henriques, Paulo de Carvalho, Teresa Rocha, Simão Paredes, João Morais
The employment of personal health systems (pHealth) is a valuable concept in the management of chronic diseases, particularly in the context of cardiovascular diseases. By means of a continuous monitoring of the patient it is possible to seamless access multiple sources of data, including physiological signals, providing professionals with a global and reliable view of the patient's status. In practice, it is possible the prompt diagnosis of events, the early prediction of critical events and the implementation of personalized therapies...
2016: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/27219820/this-is-what-i-need-a-clinical-feedback-system-to-do-for-me-a-qualitative-inquiry-into-therapists-and-patients-perspectives
#16
Christian Moltu, Marius Veseth, Jon Stefansen, Jan Christian Nøtnes, Åse Skjølberg, Per-Einar Binder, Louis Georges Castonguay, Samuel S Nordberg
: Routine outcome monitoring and clinical feedback systems (ROM/CFSs) are promising methods of providing naturalistic research data and enhancing mental health care. However, implementation in routine care is challenging, and we need more knowledge about clinicians' and patients' needs from such systems. OBJECTIVE: We aimed to study perspectives of clinicians and patients to explore how ROM/CFS can be helpful and acceptable to them. METHOD: We interviewed 55 participants in focus groups and individual interviews and analyzed the data through rigorous team-based qualitative analyses...
May 24, 2016: Psychotherapy Research: Journal of the Society for Psychotherapy Research
https://www.readbyqxmd.com/read/27164611/assessing-antidepressants-using-intelligent-data-monitoring-and-mining-of-online-fora
#17
Altug Akay, Andrei Dragomir, Bjorn-Erik Erlandsson
Depression is a global health concern. Social networks allow the affected population to share their experiences. These experiences, when mined, extracted, and analyzed, can be converted into either warnings to recall drugs (dangerous side effects), or service improvement (interventions, treatment options) based on observations derived from user behavior in depression-related social networks. Our aim was to develop a weighted network model to represent user activity on social health networks. This enabled us to accurately represent user interactions by relying on the data's semantic content...
July 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27108146/diabetes-care-in-republic-of-macedonia-challenges-and%C3%A2-opportunities
#18
REVIEW
Ivica Smokovski, Tatjana Milenkovic, Caroline Trapp, Aleksandar Mitov
BACKGROUND: The Republic of Macedonia (RoM) has experienced a rapid rise in the prevalence of type 2 diabetes (T2D) over the past 2 decades, a period characterized by significant social, political, and economic change. RoM now has one of the highest rates of diabetes in Europe. OBJECTIVES: To explore the modifiable conditions that may underlie and exacerbate the T2D epidemic; describe the state of diabetes care; and consider improved mechanisms for prevention and treatment, including research priorities, in RoM...
November 2015: Annals of Global Health
https://www.readbyqxmd.com/read/27064774/modifiable-risk-factors-in-patients-with-low-back-pain
#19
Scott T Shemory, Kiel J Pfefferle, Ian M Gradisar
Low back pain is one of the most common reasons for physician visits in the United States and is a chief complaint frequently seen by orthopedic surgeons. Patients with chronic low back pain can experience recurring debilitating pain and disability, decreasing their quality of life. A commercially available software platform, Explorys (Explorys, Inc, Cleveland, Ohio), was used to mine a pooled electronic health care database consisting of the medical records of more than 26 million patients. According to the available medical history data, 1...
May 1, 2016: Orthopedics
https://www.readbyqxmd.com/read/27063588/a-system-for-automated-outbreak-detection-of-communicable-diseases-in-germany
#20
Maëlle Salmon, Dirk Schumacher, Hendrik Burmann, Christina Frank, Hermann Claus, Michael Höhle
We describe the design and implementation of a novel automated outbreak detection system in Germany that monitors the routinely collected surveillance data for communicable diseases. Detecting unusually high case counts as early as possible is crucial as an accumulation may indicate an ongoing outbreak. The detection in our system is based on state-of-the-art statistical procedures conducting the necessary data mining task. In addition, we have developed effective methods to improve the presentation of the results of such algorithms to epidemiologists and other system users...
2016: Euro Surveillance: Bulletin Européen sur les Maladies Transmissibles, European Communicable Disease Bulletin
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