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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nala Alahmari & Sarah Alswedani & Ahmed Alzahrani & Iyad Katib & Aiiad Albeshri & Rashid Mehmood, 2022. "Musawah: A Data-Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in Saudi Arabia," Sustainability, MDPI, vol. 14(6), pages 1-41, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3313-:d:769640
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    References listed on IDEAS

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    1. Matthew J Eckelman & Jodi Sherman, 2016. "Environmental Impacts of the U.S. Health Care System and Effects on Public Health," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-14, June.
    2. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    3. Tan Yigitcanlar & Massimo Regona & Nayomi Kankanamge & Rashid Mehmood & Justin D’Costa & Samuel Lindsay & Scott Nelson & Adiam Brhane, 2022. "Detecting Natural Hazard-Related Disaster Impacts with Social Media Analytics: The Case of Australian States and Territories," Sustainability, MDPI, vol. 14(2), pages 1-23, January.
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    2. Ali Khosravi Kazazi & Fariba Amiri & Yaser Rahmani & Raheleh Samouei & Hamidreza Rabiei-Dastjerdi, 2022. "A New Hybrid Model for Mapping Spatial Accessibility to Healthcare Services Using Machine Learning Methods," Sustainability, MDPI, vol. 14(21), pages 1-18, October.
    3. Ghadah Alkhayat & Syed Hamid Hasan & Rashid Mehmood, 2022. "SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting," Energies, MDPI, vol. 15(18), pages 1-55, September.
    4. Raniah Alsahafi & Ahmed Alzahrani & Rashid Mehmood, 2023. "Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations," Sustainability, MDPI, vol. 15(5), pages 1-64, February.

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