Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach

How, Meng-Leong; Cheah, Sin Mei; Chan, Yong Jiet; Khor, Aik Cheow; Say, Eunice Mei Ping

doi:10.3390/info11010039

Cited by 40 publications

(24 citation statements)

References 49 publications

(50 reference statements)

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“…• Use of theoretically informed and empirically validated models to conduct artificial intelligence-based research [8] to consider a full spectrum of both micro (person-centered) and macro (contextual or ecological) predictors for reducing risks of having poor outcomes or hospital readmissions.…”

Section: Population Health Management Approachesmentioning

confidence: 99%

A Patient-Centric Care Approach to Facilitate the Design of an Artificial Intelligence Application in Geriatric Care Management of Heart Failure Readmissions

Wan¹

2021

BRCR

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The examination of human factors’ role in moderating medical interventions and hospitalizations and/or rehospitalization of heart failure (HF) patients. Objectives: The primary purpose of this study is two-fold: 1) to show relevant human factors influencing the rehospitalization of persons with heart failure by developing a systematic algorithm generated from the cited randomized trials; and 2) to examine how the self-care principles, such as choice/efficacy, restfulness, healing environment, activity, trust, interpersonal relationships, outlook, and nutrition, may reduce heart failure readmissions. Methods: The meta-analytic approach generated a theoretically relevant and empirically validated self-care management decision support protocol for HF. Statistical modeling of the effects of eight human factors for the reduction of HF readmissions was presented. Findings: The systematic review and meta-analysis approach documents the results of randomized clinical trials that affect heart failure hospitalization by selected human factors. A patient-centered decision support system was developed to facilitate the self-care management of heart failure. Discussion: Our research generates systematic knowledge about the importance of human-factor principles in the provision of geriatric care for heart failure. Using shared decision-making strategies under the population health management approach could enhance the quality of care and reduce costly readmissions of heart failure, particularly for elderly patients.

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Section: Population Health Management Approachesmentioning

confidence: 99%

A Patient-Centric Care Approach to Facilitate the Design of an Artificial Intelligence Application in Geriatric Care Management of Heart Failure Readmissions

Wan¹

2021

BRCR

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“…FSPs have more valuable financial data than any other industry, and process a huge volume of transactions, which is a rich source of data that can be mined to determine what customers need. This is where AI can contribute towards social good [4][5][6]. The value of the mined data could be harnessed if the conditions for success could be ranked by its relative importance.…”

Section: Advancing Financial Inclusion For Social Good Using Aimentioning

confidence: 99%

Artificial Intelligence-Enhanced Predictive Insights for Advancing Financial Inclusion: A Human-Centric AI-Thinking Approach

How

Cheah

Khor

et al. 2020

BDCC

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According to the World Bank, a key factor to poverty reduction and improving prosperity is financial inclusion. Financial service providers (FSPs) offering financially-inclusive solutions need to understand how to approach the underserved successfully. The application of artificial intelligence (AI) on legacy data can help FSPs to anticipate how prospective customers may respond when they are approached. However, it remains challenging for FSPs who are not well-versed in computer programming to implement AI projects. This paper proffers a no-coding human-centric AI-based approach to simulate the possible dynamics between the financial profiles of prospective customers collected from 45,211 contact encounters and predict their intentions toward the financial products being offered. This approach contributes to the literature by illustrating how AI for social good can also be accessible for people who are not well-versed in computer science. A rudimentary AI-based predictive modeling approach that does not require programming skills will be illustrated in this paper. In these AI-generated multi-criteria optimizations, analysts in FSPs can simulate scenarios to better understand their prospective customers. In conjunction with the usage of AI, this paper also suggests how AI-Thinking could be utilized as a cognitive scaffold for educing (drawing out) actionable insights to advance financial inclusion.

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“…The dataset comprising 19 rows of data in a dataset containing indicators from the year 2000 until 2018 about malnutrition, health, and population statistics was imported into Bayesialab. The purpose was to discover the informational motif of the data [63].…”

Section: Pre-processing: Checking For Missing Values or Errors In Thementioning

confidence: 99%

Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach

How

Chan

2020

Self Cite

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According to the World Health Organization (WHO) and the World Bank, malnutrition is one of the most serious but least-addressed development challenges in the world. Malnutrition refers to the malfunction or imbalance of nutrition, which could be influenced not only by under-nourishment, but also by over-nourishment. The significance of this paper is that it shows how artificial intelligence (AI) can be democratized to enable analysts who are not trained in computer science to also use human-centric explainable-AI to simulate the possible dynamics between malnutrition, health and population indicators in a dataset collected from 180 countries by the World Bank. This AI-based human-centric probabilistic reasoning approach can also be used as a cognitive scaffold to educe (draw out) AI-Thinking in analysts to ask further questions and gain deeper insights. In this study, a rudimentary beginner-friendly AI-based Bayesian predictive modeling approach was used to demonstrate how human-centric probabilistic reasoning could be utilized to analyze the dynamics of global malnutrition and optimize conditions for achieving the best-case scenario. Conditions of the worst-case “Black Swan” scenario were also simulated, and they could be used to inform stakeholders to prevent them from happening. Thus, the nutritional and health status of vulnerable populations could be ameliorated.

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Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach

Cited by 40 publications

References 49 publications

A Patient-Centric Care Approach to Facilitate the Design of an Artificial Intelligence Application in Geriatric Care Management of Heart Failure Readmissions

A Patient-Centric Care Approach to Facilitate the Design of an Artificial Intelligence Application in Geriatric Care Management of Heart Failure Readmissions

Artificial Intelligence-Enhanced Predictive Insights for Advancing Financial Inclusion: A Human-Centric AI-Thinking Approach

Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach

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