An Empirical Illustration of How Socioeconomic Stakeholders Can Leverage AI and Big Data

An Empirical Illustration of How Socioeconomic Stakeholders Can Leverage AI and Big Data

Kenneth David Strang
DOI: 10.4018/978-1-6684-5959-1.ch002
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This chapter illustrates how artificial intelligence (AI) can be applied to analyze big data for the benefit of socioeconomic stakeholders. The research question was how state-of-the-art AI software could be used to help decision makers, particularly after major global crises such as climate change, natural disasters, and pandemics. In this chapter, the issues and controversies of big data and AI software are discussed. After an extensive literature review, this chapter proposes, develops, and tests an AI model using big data and AI statistical software. The concept was proven with sample data and a simulation. This AI big data modeling concept is argued to be valuable to many socioeconomic stakeholders, including computer science researchers and academic scholars.
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This chapter discusses an applied example of how artificial intelligence (AI) software was used to benefit socioeconomic stakeholders using a hospital as a case study, and it illustrated how AI could generalize to any industry. This introductory section will highlight key issues in the literature in order to establish the rationale underlying the current study. Next, the relevant literature is reviewed and then a practical example is explained by analyzing medical data to predict COVID-19 in non-intensive care hospital patients. The results are discussed in terms of how AI could contribute to disease analysis and prediction – in terms of stakeholders. Thus, this chapter is intended for health informatics professionals but other healthcare stakeholders would likely benefit from these findings including computer science scholars, medical physicians, clinical researchers, healthcare methods researchers including students, and pharmaceutical companies.

The COVID-19 pandemic impacted everyone in the world. Think back to the start of the pandemic and imagine you are a computer science scholars staff assisting medical teams when numerous patients arrive to an already over-burdened hospital. Remember this mysterious COVID-19 caused patients to die quickly as compared to common ICU patient illnesses. Furthermore, it spread rapidly and invisibly. Early on in the COVID-19 pandemic, there were large volumes of computer science scholars’ data being shared but practitioners struggled with analyzing it for causes or predictions of patient morbidity and mortality. In fact, most COVID-19 analytic software breakthroughs were detection, not a prediction, accomplished by identifying particular congestion patterns from lung x-ray or CT images of sick patients (Kuchana et al., 2021; Li et al., 2020; Wang et al., 2021; Wen et al., 2020). At best this was an affirmation of what was suspected namely COVID-19 infection, or in certain cases, the lack of particular patterns instead confirmed other repertory viruses such as pneumonia.

AI has already been applied to predict viruses. Google Flu Trends was an interesting well-known case of AI being used to predict severe flu outbreaks around the world (Strang, 2021). However, there were good as well as bad results. Google uses AI programming to search for flu symptoms, remedies, and other related keywords from browser Internet searches to provide near-real-time estimates of flu activity in the United States and 24 other countries (Lazer, Kennedy, King, & Vespignani, 2014). The AI principle was to assume searches meant the user or someone in their residence was experiencing symptoms. According to Lazer, Kennedy, King and Vespignani (2014), Google Flu Trends provided a remarkably accurate indicator of the flu cases in the United States between 2009 and 2011, which was significantly more accurate than the CDC predictions. However, Google Flu Trends was inaccurate thereafter for 2012–2013, more than twice as high as the CDC predictions of which the latter were accurate (Lazer et al., 2014). Thus, self-perceived user symptoms of the virus became a proxy for a medical doctor suspecting the virus after examining a patient. The problem was many users were paranoid, falsely assuming they had a virus and not trusting a single result so multiple searches were done by them and their friends. Mild user hysteria propagated searches for the flu and inflated actual cases by many times. Thus, although Google Flu Trends AI was an accurate forecast tool over several years, it became inaccurate because the predictions for vaccinations and doctor visits increased much more than the actual need which subsequently cost health providers and medical facilities through resource waste. Therefore, computer science scholars and practitioners need more reliable methods to predict patient infections as well as virus outbreaks.

Based on the above rationale that computer science professionals need better predictive tools, the research question (RQ) became: Can AI software be applied to predict COVID-19 infection by analyzing non-hospitalized patients’ medical records? The aim of the current chapter is to explore if and how AI software techniques could process a high volume/velocity of medical big data to predict future first-time COVID-19 infection likelihood. In this chapter several relevant computer science issues are reviewed; a medical big dataset is collected then an illustrative AI statistical model is developed and applied to predict the first-time COVID-19 outbreak.

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