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Erschienen in:
Dimensionality Reduction for Exploratory Data Analysis in Daily Medical Research Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Soft Data Analytics with Fuzzy Cognitive Maps: Modeling Health Technology Adoption by Elderly Women

verfasst von : Noshad Rahimi, Antonie J. Jetter, Charles M. Weber, Katherine Wild

Erschienen in: Advanced Data Analytics in Health

Verlag: Springer International Publishing

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Abstract

Modeling how patients adopt personal health technology is a challenging problem: Decision-making processes are largely unknown, occur in complex, multi-stakeholder settings, and may play out differently for different products and users. To address this problem, this chapter develops a soft analytics approach, based on Fuzzy Cognitive Maps (FCM) that leads to adoption models that are specific for a particular product and group of adopters. Its empirical grounding is provided by a case study, in which a group of women decides whether to adopt a wearable remote healthcare monitoring device. The adoption model can simulate different product configurations and levels of support and provide insight as to what scenarios will most likely lead to successful adoption. The model can be used by product developers and rollout managers to support technology planning decisions.

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Vorheriges Kapitel An Agent-Based Model of Healthy Eating with Applications to Hypertension
Nächstes Kapitel Machine Learning for the Classification of Obesity from Dietary and Physical Activity Patterns
Fußnoten
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Detail research material can be found on OSF website (Springer Chapter book: Rahimi et al.): https://​osf.​io/​hp7r2/​
 
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Metadaten
Titel
Soft Data Analytics with Fuzzy Cognitive Maps: Modeling Health Technology Adoption by Elderly Women
verfasst von
Noshad Rahimi
Antonie J. Jetter
Charles M. Weber
Katherine Wild
Copyright-Jahr
2018
Buch
Advanced Data Analytics in Health

Print ISBN: 978-3-319-77910-2

Electronic ISBN: 978-3-319-77911-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77911-9_4