AIM in Haematology

Davids, Joseph; Ashrafian, Hutan

doi:10.1007/978-3-030-58080-3_182-1

Cited by 1 publication

(1 citation statement)

References 65 publications

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“…They investigated the landscape of smartphone apps, focusing on the enhancement and sustainment of health and wellness via four forms of supervised ML. Many industry specialists have started to analyze unstructured text data using sophisticated technologies like natural language processing (NLP), ML, and text mining (Davids & Ashrafian, 2020).…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Machine Learning to Identify Positive and Negative Themes in Jordanian mHealth Apps

Alhur

Alshamari

Oláh

et al. 2022

International Journal of E-Services and Mobile Applications

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User opinions are crucial in the development of mobile health (mHealth) applications (apps). This study aimed to investigate and qualitatively assess consumer attitudes toward mHealth apps and the main aspects of their design. The methodology was divided into four steps: (1) data collection, (2) preprocessing, (3) sentiment analysis by valence-aware dictionary and sentiment reasoner (VADER), and (4) thematic analysis by the latent Dirichlet allocation (LDA) algorithm. These steps were implemented in 836 reviews of eight mHealth apps on app stores in Jordan. The current study offers healthcare stakeholders insight into the positive and negative aspects of mHealth apps by identifying user-preferred features and recommending improvements. The findings indicate several aspects of design that mHealth app developers may use to improve overall efficacy, including user experience, client services, usability, and adherence.

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Section: Related Workmentioning

confidence: 99%