Musawah: A Data-Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in Saudi Arabia
Sustainability
; 14(6):3313, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1765872
ABSTRACT
The sustainability of human existence is in dire danger and this threat applies to our environment, societies, and economies. Smartization of cities and societies has the potential to unite individuals and nations towards sustainability as it requires engaging with our environments, analyzing them, and making sustainable decisions regulated by triple bottom line (TBL). Poor healthcare systems affect individuals, societies, the planet, and economies. This paper proposes a data-driven artificial intelligence (AI) based approach called Musawah to automatically discover healthcare services that can be developed or co-created by various stakeholders using social media analysis. The case study focuses on cancer disease in Saudi Arabia using Twitter data in the Arabic language. Specifically, we discover 17 services using machine learning from Twitter data using the Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-services, namely, Prevention, Treatment, Psychological Support, Socioeconomic Sustainability, and Information Availability. Subsequently, we show the possibility of finding additional services by employing a topical search over the dataset and have discovered 42 additional services. We developed a software tool from scratch for this work that implements a complete machine learning pipeline using a dataset containing over 1.35 million tweets we curated during September–November 2021. Open service and value healthcare systems based on freely available information can revolutionize healthcare in manners similar to the open-source revolution by using information made available by the public, the government, third and fourth sectors, or others, allowing new forms of preventions, cures, treatments, and support structures.
Environmental Studies; machine learning; big data analytics; social media; Twitter; smart healthcare; cancer; Arabic language; Latent Dirichlet Allocation (LDA); topic modeling; Natural Language Processing (NLP); smart cities; Artificial intelligence; Datasets; Sustainability; Cancer therapies; Social network analysis; Health care; Social networks; Data analysis; Disease prevention; Health services; Ethics; Health care industry; Learning algorithms; COVID-19; Case studies; Software development tools; Innovations; Decision analysis; Pandemics; Algorithms; Stakeholders; Software; Dirichlet problem; Data sets; Saudi Arabia
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Case report
Language:
English
Journal:
Sustainability
Year:
2022
Document Type:
Article
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