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About 10,000 papers for “eHealth”

eHealth

Christoph Engemann · 2020

Cited by 1

eHealth

Constance H.C. Drossaert, Julia E.W.C. van Gemert-Pijnen · University of Twente Research Information · 2010

Cited by 0

eHealth

Drossaert, Constance H.C., van Gemert-Pijnen, Julia E.W.C., Lechner, Lilian et al. · Data Archiving and Networked Services (DANS) · 2010

Cited by 0

eHealth

Dol, Aranka · Data Archiving and Networked Services (DANS) · 2014

Cited by 0Open access

eHealth

Drossaert, Constance H.C., van Gemert-Pijnen, Julia E.W.C. · University of Twente Research Information · 2010

Cited by 0

eHealth

Natalia Pedersen, Peter Thielsen, L. Martinsen et al. · Inflammatory Bowel Diseases · 2014

Web-guided therapy with mesalazine in mild-to-moderate ulcerative colitis helps to individualize the dose and improve adherence to therapy. The study confirms mesalazine efficacy in mild-to-moderate UC, significantly improving TIBS values in majority of the patients.

Cited by 92Open access

eHealth

Drossaert, Constance H.C., van Gemert-Pijnen, Julia E.W.C. · University of Twente Research Information · 2010

Cited by 0

eHealth

Christoph Engemann · 2018

Cited by 0

eHealth

Dol, Aranka · Data Archiving and Networked Services (DANS) · 2015

Cited by 0

eHealth

Volker P. Andelfinger, Till Hänisch · RePEc: Research Papers in Economics

Cited by 0

eHealth

Ria Wijnen, Toon Verlaan · Geron · 2017

Cited by 0

eHealth

Heidi D. Blossom, Alex Neal · 2018

The convergence of digital technologies in the area of healthcare is creating an age of digital health that will be highly individualized through the use of mobile communication, wireless sensors, wearable wireless devices, and super networks of big data. This convergence allows diagnostics not solely based on symptoms, but rather on up-to-the-minute individual health factors. Micro sensors built into clothing and wearable devices send health data to Internet capable devices or directly to the cloud and connect users, caregivers, and healthcare providers to vital signs that could allow for better healthcare dec is ions. Th is is “Big Health Data,” and it is bringing our health information together in ways that will improve personal health and aid in advancements in artificial intelligence and robotics which have the potential of saving lives through innovative diagnostic and treatments. No matter how you look at it, eHealth is driving healthcare transformation with technological innovations that are saving lives and reducing healthcare costs.

Cited by 0

Workshop eHealth en leefstijlverandering: eHealth week 2020

Alpay, Laurence, Koster, Ybranda, Dallinga, Joan et al. · Data Archiving and Networked Services (DANS) · 2020

Het vakgebied van de persuasieve technologie richt zich op design, ontwikkeling en evaluatie van interactieve technologieën die bedoeld zijn om de houding of het gedrag van de patiënt/ cliënt te veranderen door middel van overtuiging en sociale invloed. Het is bij uitstek een interdisciplinair onderzoeksgebied waar psychologie, mens-computer interactie en vakgebieden uit zorg en welzijn samen opereren. Het doel van de workshop was om gezamenlijk te verkennen hoe impactmeting van persuasieve technologie uitgevoerd kan worden.

Cited by 0Open access

eHealth Literacy

Matthew N. O. Sadiku, Mahamadou Tembely, Sarhan M. Musa et al. · International Journal of Advanced Research in Computer Science and Software Engineering · 2017

Cited by 2

ehealth literacy

Dorit Alt · Data Archiving and Networked Services (DANS) · 2021

ehealth literacy

Cited by 0Open access

Thinking eHealth

Izabella Lokshina, Michael R. Bartolacci · International Journal of Interdisciplinary Telecommunications and Networking · 2014

This paper focuses on a mathematical background of an individual health status monitoring system to empower young people to manage their health. The proposed health status monitoring system uses symptoms observed with mobile sensing devices and prior information about health and environment (provided it exists) to define individual physical and psychological status. It assumes that a health status identification process is influenced by many parameters and conditions. It has a flexible logical inference system providing positive psychological influence on young people since full acceptance of recommendations on their behavioral changes towards healthy lifestyles is reached and a correct interpretation is guaranteed. The model and algorithms of the individual health status monitoring system are developed based on the composition inference rule in Zadeh's fuzzy logic. The model allows us to include in the algorithms of logical inference the possibility of masking (by means of a certain health condition) the symptoms of other health situations as well as prior information (if it exists) regarding health and environment. The algorithms are generated by optimizing the truth of a single natural “axiom”, which connects an individual health status (represented by classes of health situations) with symptoms and matrices of influence of health situations on symptoms and masking of symptoms. The new algorithms are fairly different from traditional algorithms, in which the result is produced in the course of numerous single processing rules. Therefore, the use of a composition inference rule makes a health status identification process faster and the obtained results more precise and efficient comparing to traditional algorithms.

Cited by 3Open access