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

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.

Discovering Urban Governance Parameters for Online Learning in Saudi Arabia During COVID-19 Using Topic Modeling of Twitter Data

“…It is clear that while these parameters are specific to online learning, these broadly fall into the general policies and methods used in urban governance. Secondly, it supports the premise that research and practice in smart cities and urban governance should be driven by data (Liu et al, 2017;Bibri, 2021;Yigitcanlar et al, 2021b) and confirms that digital media including social networks data are important sources of data that could be used for smart urban governance (Barns, 2020;Ahmad et al, 2022;Alahmari et al, 2022;Yigitcanlar et al, 2022).…”

Sustainable Participatory Governance: Data-Driven Discovery of Parameters for Planning Online and In-Class Education in Saudi Arabia During COVID-19

“…We have built significant capacity in data-driven urban computing research such as our research on improving machine learning-based methods, and application of these methods, and the use of emerging technologies in smart societies and several urban sectors (Mehmood et al, 2017b(Mehmood et al, , 2020; for example, see Alam et al, 2017;Alyahya et al, 2020;Arfat et al, 2020;Ahmad et al, 2022;Alahmari et al, 2022;Janbi et al, 2022). We will continue to build this capacity further with our mission of contributing to the international efforts on developing smarter sustainable societies.…”

Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery using BERT Modelling

“…Within the specific focus of this paper, we introduced in [37] the concept of Deep Journalism and discovered public, academic, and industry perspectives on transportation using The Guardian, Web of Science, and Traffic Technology International Magazine, respectively. We have also discovered parameters for education and learning during the COVID-19 pandemic [38] and healthcare services for cancer [39]. The work presented in this paper is novel for several reasons.…”