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Bern University of Applied Sciences, Switzerland
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5 Papers

A Study about Discovery of Critical Food Consumption Patterns Linked with Lifestyle Diseases using Data Mining Methods

Topics: Datamining; Decision Support Systems; Clinical Problems and Applications; Pattern Recognition and Machine Learning; Practice-based Research Methods for Healthcare IT

Authors: Farshideh Einsele 1 , Leila Sadeghi 2 , Rolf Ingold 3 and Helena Jenzer 2

Affiliations: 1 Berne University of Applied Sciences, Switzerland , 2 Bern University of Applied Sciences, Switzerland , 3 University of Fribourg, Switzerland

Digital Object Identifier: 10.5220/0005170402390245

Proceedings of HEALTHINF 2015/BIOSTEC, ISBN: 978-989-758-068-0

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Background: To date, the analysis of the implications of dietary patterns on lifestyle diseases is based on data coming either from clinical studies or food surveys, both comprised of a limited number of participants. This article demonstrates that linking big data from a grocery store sales database with demographical and health data by using data mining tools such as classification and association rules is a powerful way to determine if a specific population subgroup is at particular risk for developing a lifestyle disease based on its food consumption patterns. Objective: The objective of the study was to link big data from grocery store sales with demographic and health data to discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food consumption. Design: Food consumption databases from a publicly available grocery store database dating from 1997–1998 were gathered along with corresponding demographics and health data from the U. S. west coast, pre-processed, cleaned and finally integrated to a unique database. Results: This study applied data mining techniques such as classification and association mining analysis. Firstly, the studied population was classified according to the demographical information “ age groups” and “race” and data for lifestyle diseases were correspondingly attributed. Secondly, association mining analysis was used to incorporate rules about food consumption and lifestyle diseases. A set of promising preliminary rules and their corresponding interpretation was generated and reported in the present paper. Conclusions: Association mining rules were successfully used to describe and predict rules linking food consumption patterns with lifestyle diseases. In the selected grocery store database, information about interesting aspects of the grocery store customers were found such as marital status, educational background, profession and number of children at home. An in-depth research on these attributes is needed to further expand the present demographical database. Since the search on the internet for demographical attributes back to the year of 2000 corresponding to the studied population subgroup was extremely laborious, the selected demographical attributes to prove the feasibility of the study were limited to age groups and race.
Collapse Button Related Ontology Subjects/Areas/Topics (16)
Artificial Intelligence

Databases and Information Systems Integration

Pattern Recognition and Machine Learning

Biomedical Engineering

Datamining

Practice-based Research Methods for Healthcare IT

Business Analytics

Decision Support Systems

Sensor Networks

Clinical Problems and Applications

Enterprise Information Systems

Signal Processing

Data Engineering

Health Information Systems

Soft Computing
Data Mining

Discrimination of Healthy and Post-partum Subjects using Wavelet Filterbank and Auto-regressive Modelling

Topics: Medical Signal Acquisition, Analysis and Processing; Wavelet Transform

Authors: Rolf Vetter 1 , Jonas Schild 1 , Annette Kuhn 2 and Lorenz Radlinger 3

Affiliations: 1 Bern University of Applied Siences, Switzerland , 2 Bern University Hospital and University of Bern, Switzerland , 3 Bern University of Applied Sciences, Switzerland

Digital Object Identifier: 10.5220/0005176301320137

Proceedings of BIOSIGNALS 2015/BIOSTEC, ISBN: 978-989-758-069-7

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Rehabilitation therapies to treat female stress urinary incontinence focus on the reactivation of pelvic floor muscle (PFM) activity. An objective measure is essential to assess a subject’s improvement in PFM capabilities and increase the success rate of the therapy. In order to provide such a measure, we propose a method for the discrimination of healthy subjects with strong PFM and post-partum subjects with weak PFM. Our method is based on a dyadic discrete wavelet decomposition of electromyograms (EMG) that projects slow-twitched and fast-twitched muscle activities onto different scales. We used a parametric auto-regressive (AR) model for the estimation of the frequency of each wavelet scale to overcome the poor frequency resolution of the dyadic decomposition. The feature used for discrimination was the frequency of the wavelet scale with the highest variance after interpolation with the nearest neighboring scales. Twenty-three healthy and 26 post-partum women with weak PFM who executed 4 maximum voluntary contractions (MVC) were retrospectively analysed. EMGs were recorded using a vaginal probe. The proposed method has a lower rate of false discrimination (4%) compared to the two classical methods based on mean (9%) and median (7%) frequency estimation from the power spectral density.
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Applications and Services

Biomedical Signal Processing

Medical Image Detection, Acquisition, Analysis and Processing

Biomedical Engineering

Computer Vision, Visualization and Computer Graphics

Wavelet Transform

Research and Engineering Roadmap for Development and Deployment of Smart Medical Devices

Authors: Jan Sliwa and Emmanuel Benoist

Affiliation: Bern University of Applied Sciences, Switzerland

Digital Object Identifier: 10.5220/0005291002210232

Proceedings of SmartMedDev 2015/BIOSTEC, ISBN: 978-989-758-071-0

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The actual clinical use of smart wireless, software-based, mobile medical devices does not meet the recently raised expectations. First, current low level of interoperability calls for setting and enforcing open standards from the device level to the national/global collaboration structure. Second, heterogeneous and frequently changing devices, operating in various natural, technical and human environments, do not match the classical approval model. In addition to a time-limited set of clinical trials, they need a system of continuous quality monitoring. Third, ad-hoc deployment, without dedicated staff with well defined, novel skill sets is not scalable. A proper organizational structure is necessary. In this paper we present a modular software structure and a framework of a system supporting both the direct health care and the continuous quality evaluation. We expose the location of interfaces crucial for assuring multivendor interoperability. We then define a roadmap giving structure to the necessary development effort. The structure we propose should permit to coordinate the actions of independent teams tackling the immense number of multifaceted and interrelated tasks.

How to Collect Consent for an Anonymous Medical Database

Topics: Confidentiality and Data Security; Databases and Datawarehousing; e-Health; Security and Medical Data Access-Control

Authors: Emmanuel Benoist and Jan Sliwa

Affiliation: Bern University of Applied Sciences - IT, Switzerland

Digital Object Identifier: 10.5220/0004902404050412

Proceedings of HEALTHINF 2014/BIOSTEC, ISBN: 978-989-758-010-9

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The goal of some medical databases is not to support the actual treatment of individual patients, but to provide the platform for medical research. Health data collected in such databases have to be anynomized - they should be analyzed only statistically and should not permit to retrieve the patient’s identity. Medical data collected for research should be anonymized to protect the patients’ privacy. In many countries it is mandatory. In many cases, not only one person treats a patient for a given illness. The documentation of a case requires the collaboration of different physicians that share information. This sharing of information requires the patient to authorise the access to the data stored by one physician by another one. We need therefore to implement a system for collecting the consent of an anonymous person. We present a novel solution to allow the practitioner to collect the consent of the patient in order to access the data recorded for that person. This solution is based on existing infrastructure, such as X509 certificates (present in e-ID or e-Health cards). Patients do not require to acquire any new hardware or to remember any new secret. We produce the fingerprint of the private key of the patient that can be used to re-identify the patient without having to know the identity of the patient (for instance the certificate) or even the patient’s public key.
Collapse Button Related Ontology Subjects/Areas/Topics (7)
Biomedical Engineering

Databases and Datawarehousing

Health Information Systems

Cloud Computing

e-Health

Platforms and Applications
Confidentiality and Data Security

Management of Multiple Data Streams in Sensor Networks for Medicine

Topics: Telemedicine; Confidentiality and Data Security; Mobile Technologies for Healthcare Applications; e-Health; Pervasive Health Systems and Services

Authors: Jan Sliwa and Emmanuel Benoist

Affiliation: Bern University of Applied Sciences, Switzerland

Digital Object Identifier: 10.5220/0004325403990402

Proceedings of HEALTHINF 2013/BIOSTEC, ISBN: 978-989-8565-37-2

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In this paper we discuss the privacy aware management of data streams generated by wireless sensor networks (WSN) in medical applications. Such data are primarily used to ensure the direct health support for a specific patient. They can also be used for other purposes, like quality assurance of applied devices and comparative evaluation research of medical treatments. Considering the novelty of the WSN-based medical application, every information helping to improve the service is extremely useful. As privacy in medicine is an important issue, care should be taken to harmonize the legal requirements with the medical necessities. Data used for different purposes have different characteristics, different users and different access rules. Therefore the raw data stream has to be split and processed (condensed, anonymized, etc.) in various ways dependent on the use. The access to data has to defined differently for different output data streams.
Collapse Button Related Ontology Subjects/Areas/Topics (15)
Biomedical Engineering

Health Engineering and Technology Applications

Neurotechnology, Electronics and Informatics

Cloud Computing

Health Information Systems

Pervasive Health Systems and Services

Confidentiality and Data Security

Mobile Technologies

Platforms and Applications

Distributed and Mobile Software Systems

Mobile Technologies for Healthcare Applications

Software Engineering

e-Health

Neural Rehabilitation

Telemedicine

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