Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis

Johnson, Marina; Albizri, Abdullah; Simsek, Serhat

doi:10.1007/s10479-020-03872-6

Cited by 31 publications

(15 citation statements)

References 58 publications

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“…K-fold CV is used to validate the AI models and prevent overfitting (Johnson et al , 2020; Simsek et al , 2021). A completely independent dataset is needed to test the performance of the AI models after K-fold CV.…”

Section: Methodsmentioning

confidence: 99%

Digital transformation to mitigate emergency situations: increasing opioid overdose survival rates through explainable artificial intelligence

Johnson¹,

Albizri²,

Harfouche

et al. 2021

IMDS

Self Cite

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PurposeThe global health crisis represents an unprecedented opportunity for the development of artificial intelligence (AI) solutions. This paper aims to integrate explainable AI into the decision-making process in emergency scenarios to help mitigate the high levels of complexity and uncertainty associated with these situations. An AI solution is designed to extract insights into opioid overdose (OD) that can help government agencies to improve their medical emergency response and reduce opioid-related deaths.Design/methodology/approachThis paper employs the design science research paradigm as an overarching framework. Open-access digital data and AI, two essential components within the digital transformation domain, are used to accurately predict OD survival rates.FindingsThe proposed AI solution has two primary implications for the advancement of informed emergency management. Results show that it can help not only local agencies plan their resources for timely response to OD incidents, thus improving survival rates, but also governments to identify geographical areas with lower survival rates and their primary contributing factor; hence, they can plan and allocate long-term resources to increase survival rates and help in developing effective emergency-related policies.Originality/valueThis paper illustrates that digital transformation, particularly open-access digital data and AI, can improve the emergency management framework (EMF). It also demonstrates that the AI models developed in this study can identify opioid OD trends and determine the significant factors improving survival rates.

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Section: Methodsmentioning

confidence: 99%

Digital transformation to mitigate emergency situations: increasing opioid overdose survival rates through explainable artificial intelligence

Johnson¹,

Albizri²,

Harfouche

et al. 2021

IMDS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Likewise, Fan et al (2018), analyzed the variables affecting health professionals to adopt an AI-based medical diagnosis support system, called AIMDSS utilizing the unified theory of user acceptance of technology (UTAUT). Johnson et al (2020) developed a multi-stage framework to construct an AI-based decision support tool that can assist to predict the five-year survivability of lung cancer patients. The framework is included Logistic regression, Decision trees, Random Forest, Adaptive Boosting (AdaBoost), Artificial Neural networks, and Naïve Bayes.…”

Section: Related Workmentioning

confidence: 99%

Extending artificial intelligence research in the clinical domain: a theoretical perspective

2022

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Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.

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“…To improve patients' well-being (quality of life), healthcare services require their continuous active participation. The customers' active participation in healthcare service operations process helps to increase the influence of big data through healthcare information systems (Malik et al, 2018) which is useful in data-driven decision making (Oztekin, 2018), resource use, operational performance, and quality of service (Johnson et al, 2020). If patients stop using services due to resource misintegration that results in VCD between healthcare service providers and patients, they will not recover, which may lead to bigger problems later on and result in unexpected additional costs not only to the patient but also to their families, the organisation, and the society.…”

Section: Introductionmentioning

confidence: 99%

Value co-destruction causing customers to stop service usage: a topic modelling analysis of dental service complaint data

Goto

Belal²,

Shirahada³

2022

Ann Oper Res

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This study aims to identify the types of value co-destruction (VCD) emerging in healthcare services that cause patients to reduce or extinguish their intentions to continue using the services; it also aims to identify the VCD antecedents. Complaints from 1075 dental clinic patients, which are collected as textual data, are analysed in this study. The authors adopt an exploratory approach comprising a quantitative analysis based mainly on the topic model, a type of machine learning, and a qualitative analysis based on the KJ method. Twelve types of VCD were empirically identified, three of which had a significant negative effect on the intention to continue using the service. Ten antecedents that cause these types of VCD were identified, when examined based on a multi-level perspective, institutional factors and social norms were found to be related to the VCD process. This study contributes to understanding the mechanisms by which failures in healthcare services occur and to developing effective decision making to overcome them.

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Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis

Cited by 31 publications

References 58 publications

Digital transformation to mitigate emergency situations: increasing opioid overdose survival rates through explainable artificial intelligence

Digital transformation to mitigate emergency situations: increasing opioid overdose survival rates through explainable artificial intelligence

Extending artificial intelligence research in the clinical domain: a theoretical perspective

Value co-destruction causing customers to stop service usage: a topic modelling analysis of dental service complaint data

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