Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance

Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can spread to a larger population. Instead of detecting unexpected increases of infections by monitoring confirmed cases, syndromic surveillance aims at the detection of cases with early symptoms, which allows a more timely disclosure of outbreaks. However, the definition of these disease patterns is often challenging, as early symptoms are usually shared among many diseases and a particular disease can have several clinical pictures in the early phase of an infection. As a first step toward the goal to support epidemiologists in the process of defining reliable disease patterns, we present a novel, data-driven approach to discover such patterns in historic data. The key idea is to take into account the correlation between indicators in a health-related data source and the reported number of infections in the respective geographic region. In an preliminary experimental study, we use data from several emergency departments to discover disease patterns for three infectious diseases. Our results show the potential of the proposed approach to find patterns that correlate with the reported infections and to identify indicators that are related to the respective diseases. It also motivates the need for additional measures to overcome practical limitations, such as the requirement to deal with noisy and unbalanced data, and demonstrates the importance of incorporating feedback of domain experts into the learning procedure.

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EHR Big Data Deep Phenotyping

Yearbook of Medical Informatics ◽

10.15265/iy-2014-0006 ◽

2014 ◽

Vol 23 (01) ◽

pp. 206-211 ◽

Cited By ~ 17

Author(s):

L. Lenert ◽

G. Lopez-Campos ◽

L. J. Frey

Keyword(s):

Big Data ◽

Medical Records ◽

Clinical Findings ◽

Software Systems ◽

Related Data ◽

Processing Power ◽

Health Related ◽

Hardware Processing ◽

Definition Of ◽

Deep Phenotyping

Summary Objectives: Given the quickening speed of discovery of variant disease drivers from combined patient genotype and phenotype data, the objective is to provide methodology using big data technology to support the definition of deep phenotypes in medical records. Methods: As the vast stores of genomic information increase with next generation sequencing, the importance of deep phenotyping increases. The growth of genomic data and adoption of Electronic Health Records (EHR) in medicine provides a unique opportunity to integrate phenotype and genotype data into medical records. The method by which collections of clinical findings and other health related data are leveraged to form meaningful phenotypes is an active area of research. Longitudinal data stored in EHRs provide a wealth of information that can be used to construct phenotypes of patients. We focus on a practical problem around data integration for deep phenotype identification within EHR data. The use of big data approaches are described that enable scalable markup of EHR events that can be used for semantic and temporal similarity analysis to support the identification of phenotype and genotype relationships. Conclusions: Stead and colleagues’ 2005 concept of using light standards to increase the productivity of software systems by riding on the wave of hardware/processing power is described as a harbinger for designing future healthcare systems. The big data solution, using flexible markup, provides a route to improved utilization of processing power for organizing patient records in genotype and phenotype research.

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Novel Visualization of Large Health Related Data Sets

10.21236/ada624744 ◽

2015 ◽

Author(s):

William E. Hammond ◽

Vivian L. West ◽

David Borland ◽

Igor Akushevich ◽

Eugenia M. Heinz

Keyword(s):

Data Sets ◽

Related Data ◽

Health Related

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Real-Time Health-related Data, Wearable Medical Sensor Devices, and Smart Cyber-Physical Systems

American Journal of Medical Research ◽

10.22381/ajmr6220193 ◽

2019 ◽

Vol 6 (2) ◽

pp. 25

Keyword(s):

Real Time ◽

Cyber Physical Systems ◽

Physical Systems ◽

Related Data ◽

Sensor Devices ◽

Health Related ◽

Wearable Medical

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Assessment of Dietary Habits, Physical Activity and Lifestyle in Medical University Students

Sustainability ◽

10.3390/su13063572 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3572

Author(s):

Lavinia-Maria Pop ◽

Magdalena Iorga ◽

Iulia-Diana Muraru ◽

Florin-Dumitru Petrariu

Keyword(s):

Physical Activity ◽

Medical Students ◽

Fast Food ◽

Dietary Habits ◽

Healthy Life ◽

Related Data ◽

The Third ◽

Health Related ◽

Medical University ◽

Busy Schedule

A busy schedule and demanding tasks challenge medical students to adjust their lifestyle and dietary habits. The aim of this study was to identify dietary habits and health-related behaviours among students. A number of 403 students (80.40% female, aged M = 21.21 ± 4.56) enrolled in a medical university provided answers to a questionnaire constructed especially for this research, which was divided into three parts: the first part collected socio-demographic, anthropometric, and medical data; the second part inquired about dietary habits, lifestyle, sleep, physical activity, water intake, and use of alcohol and cigarettes; and the third part collected information about nutrition-related data and the consumption of fruit, vegetables, meat, eggs, fish, and sweets. Data were analysed using SPSS v24. Students usually slept M = 6.71 ± 1.52 h/day, and one-third had self-imposed diet restrictions to control their weight. For both genders, the most important meal was lunch, and one-third of students had breakfast each morning. On average, the students consumed 1.64 ± 0.88 l of water per day and had 220 min of physical activity per week. Data about the consumption of fruit, vegetables, meat, eggs, fish, sweets, fast food, coffee, tea, alcohol, or carbohydrate drinks were presented. The results of our study proved that medical students have knowledge about how to maintain a healthy life and they practice it, which is important for their subsequent professional life.

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A critical discourse analysis of health in Swedish integration policies

European Journal of Public Health ◽

10.1093/eurpub/ckaa166.1241 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

S Svanholm ◽

E Viitasara ◽

H Carlerby

Keyword(s):

Discourse Analysis ◽

Critical Discourse Analysis ◽

Health Behaviours ◽

Critical Discourse ◽

Healthcare Sector ◽

Integration Policy ◽

Integration Policies ◽

Health Related ◽

Definition Of ◽

The Individual

Abstract Background Previous research has indicated that migrants risk facing inequities both internationally and in Sweden; integration policies are therefore important to study. How health is described in policies affects how health interventions are approached. A discourse analysis offers a way of understanding how health is framed within the integration policies of the Establishment Program. The aim was to critically analyse the health discourses used in Swedish and European Union (EU) integration policies. Methods A critical discourse analysis, inspired by Fairclough, was performed on integration policies related to Sweden, on local, regional, national and the EU level. The policies of the Establishment Program, which focuses on newly arrived migrants (refugees, persons of subsidiary protection and their relatives who arrived through family reunification), were chosen for the analysis, and 17 documents were analysed in total. Results The analysis of the documents showed that although no definition of health was presented, health discourses were expressed in the form of the medicalization of health and the individualization of health. This not only by the terminology used, but also in how the healthcare sector was considered responsible for any health related issue and how individual health behaviours were of focus in interventions to promote health. Conclusions A pathogenic approach to health was visible in the policies and individual disease prevention was the main health focus. The results showed similarities to previous research highlighting how a particular understanding of health in a neoliberal context is formed. Key messages Health as a resource is missing in the integration policy documents. Viewing health as an individual quality puts the responsibility of promoting health on the individual.

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Food intake, physical activity and quality of life among children and youth

Work ◽

10.3233/wor-213492 ◽

2021 ◽

pp. 1-10

Author(s):

Emília Martins ◽

Rosina Fernandes ◽

Francisco Mendes ◽

Cátia Magalhães ◽

Patrícia Araújo

Keyword(s):

Quality Of Life ◽

Physical Activity ◽

Healthy Lifestyle ◽

Eating Habits ◽

Age Groups ◽

Related Quality ◽

Health Related ◽

Life Habits ◽

Definition Of

BACKGROUND: The health-related quality of life construct (QoL) implies a relationship with eating habits (EA) and physical activity (PA). Sociodemographic and anthropometric variables (gender, age and Body Mass Index - BMI) are highlighted in the definition of healthy lifestyle habits promotion strategies. OBJECTIVE: We aim to characterize and relate PA, EA and QoL in children/youth and explore gender, age and BMI influences. METHODS: It is a non-experimental study, with 337 children/youth, ages between 8 and 17 years (12.61±2.96), mostly from the rural inland of Portugal. In data collection we used a sociodemographic and anthropometric questionnaire, a weekly register table of EA and Kid-Kindl (QoL). Statistical analysis (p < 0.05) were performed in SPSS-IBM 25. RESULTS: Lower BMI was associated with better EA (p < 0.001), PA (p < 0.05) and self-esteem (p < 0.01) and worse scores on family subscale of QoL. Female showed higher fruit intake (p < 0.05). The older has shown better results. PA is positively correlated with QoL (p < 0.01) and EA (p < 0.05). CONCLUSIONS: It is important to explore other relevant social and family dimensions, to promote intervention programs with parents, school and community, as well as healthy practices policies. The intervention in these age groups is critical for a longer-term impact in improving healthy life habits.

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The definition of an atmospheric database for Aeolus

Atmospheric Measurement Techniques ◽

10.5194/amt-4-67-2011 ◽

2011 ◽

Vol 4 (1) ◽

pp. 67-88 ◽

Cited By ~ 5

Author(s):

G. J. Marseille ◽

K. Houchi ◽

J. de Kloe ◽

A. Stoffelen

Keyword(s):

High Resolution ◽

Meteorological Data ◽

Sampling Strategy ◽

Laser Wavelength ◽

Geographic Region ◽

Atmospheric Dynamics ◽

Specific Humidity ◽

Small Scale ◽

Horizontal Line ◽

Definition Of

Abstract. The definition of an atmospheric database is an important component of simulation studies in preparation of future earth observing remote sensing satellites. The Aeolus mission, formerly denoted Atmospheric Dynamics Mission (ADM) or ADM-Aeolus, is scheduled for launch end of 2013 and aims at measuring profiles of single horizontal line-of-sight (HLOS) wind components from the surface up to about 32 km with a global coverage. The vertical profile resolution is limited but may be changed during in-orbit operation. This provides the opportunity of a targeted sampling strategy, e.g., as a function of geographic region. Optimization of the vertical (and horizontal) sampling strategy requires a characterization of the atmosphere optical and dynamical properties, more in particular the distribution of atmospheric particles and their correlation with the atmospheric dynamics. The Aeolus atmospheric database combines meteorological data from the ECMWF model with atmosphere optical properties data from CALIPSO. An inverse algorithm to retrieve high-resolution particle backscatter from the CALIPSO level-1 attenuated backscatter product is presented. Global weather models tend to underestimate atmospheric wind variability. A procedure is described to ensure compatibility of the characteristics of the database winds with those from high-resolution radiosondes. The result is a high-resolution database of zonal, meridional and vertical wind, temperature, specific humidity and particle and molecular backscatter and extinction at 355 nm laser wavelength. This allows the simulation of small-scale atmospheric processes within the Aeolus observation sampling volume and their impact on the quality of the retrieved HLOS wind profiles. The database extends over four months covering all seasons. This allows a statistical evaluation of the mission components under investigation. The database is currently used for the development of the Aeolus wind processing, the definition of wind calibration strategies and the optimization of the Aeolus sampling strategy.

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Understanding Data Collection Mechanisms Used by Health and Wellness Applications (Preprint)

10.2196/preprints.30468 ◽

2021 ◽

Author(s):

Ben Philip ◽

Mohamed Abdelrazek ◽

Alessio Bonti ◽

Scott Barnett ◽

John Grundy

Keyword(s):

Data Collection ◽

User Experience ◽

Health Data ◽

Domain Model ◽

Health And Wellness ◽

Related Services ◽

Related Data ◽

Medical Apps ◽

App Development ◽

Health Related

UNSTRUCTURED Our objective is to better understand health-related data collection across different mHealth app categories. This would help in developing a health domain model for mHealth apps to facilitate app development and data sharing between these apps to improve user experience and reduce redundancy in data collection. We identified app categories listed in a curated library which was then used to explore the Google Play Store for health/medical apps that were then filtered using our inclusion criteria. We downloaded and analysed these apps using a script we developed around the popular AndroGuard tool. We analysed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects/generates health data. We retrieved 3,251 applications meeting our criteria, and our analysis showed that only 10.7% of these apps requested permission for Bluetooth access. We found 50.9% of the Bluetooth Service UUIDs to be known in these apps, with the remainder being vendor specific. The most common health-related services using the known UUIDs were Heart Rate, Glucose and Body Composition. App permissions show the most used device module/sensor to be the camera (20.57%), closely followed by GPS (18.39%). Our findings are consistent with previous studies in that not many health apps were found to use built-in sensors or peripherals for collecting health data. The use of more peripherals and automated data collection along with integration with other apps could increase usability and convenience which would eventually also improve user experience and data reliability.

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Emerging Disease Syndromic Surveillance for Hurricane Katrina Evacuees Seeking Shelter in Houston's Astrodome and Reliant Park Complex

Public Health Reports ◽

10.1177/003335490912400304 ◽

2009 ◽

Vol 124 (3) ◽

pp. 364-371 ◽

Cited By ~ 31

Author(s):

Kristy O. Murray ◽

Cindy Kilborn ◽

Mary desVignes-Kendrick ◽

Erin Koers ◽

Valda Page ◽

...

Keyword(s):

Public Health ◽

Infectious Diseases ◽

Hurricane Katrina ◽

New Orleans ◽

Acute Gastroenteritis ◽

Surveillance System ◽

Syndromic Surveillance ◽

Emerging Disease ◽

Health Concern ◽

Public Health Concern

Transmission of infectious diseases became an immediate public health concern when approximately 27,000 New Orleans-area residents evacuated to Houston's Astrodome and Reliant Park Complex following Hurricane Katrina. This article presents a surveillance system that was rapidly developed and implemented for daily tracking of various symptoms in the evacuee population in the Astrodome “megashelter.” This system successfully confirmed an outbreak of acute gastroenteritis and became a critical tool in monitoring the course of this outbreak.

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