Musawah: A Data-Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in Saudi Arabia

Nala Alahmari, Sarah Alswedani, Ahmed Alzahrani, Iyad Katib, Aiiad Albeshri and Rashid Mehmood
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Nala Alahmari: Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Sarah Alswedani: Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Ahmed Alzahrani: Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Iyad Katib: Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Aiiad Albeshri: Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Rashid Mehmood: High-Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Sustainability, 2022, vol. 14, issue 6, 1-41

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.

Keywords: 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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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