Expert system application for prioritizing preventive actions for shift work: shift expert

Esen, Hatice; Hatipoğlu, Tuğçen; Cihan, Ahmet; Fığlalı, Nilgün

doi:10.1080/10803548.2017.1350392

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…For example, researchers built an expert system that identifies the characteristics that contribute to negative effects of shift work and then selects mitigation efforts according to their importance in preventing these negative effects. With a fuzzy analytic hierarchy process model, the shift “expert” prioritizes prevention advice to shift workers at the individual and organization level [ 73 ].…”

Section: Other Use Cases For Artificial Intelligence and Sleep Medicinementioning

confidence: 99%

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Bandyopadhyay

Goldstein

2022

Sleep Breath

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Background The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. Method The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. Results Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI’s generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. Conclusion Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

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Section: Other Use Cases For Artificial Intelligence and Sleep Medicinementioning

confidence: 99%

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Bandyopadhyay

Goldstein

2022

Sleep Breath

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“…Meditation, as a form of mental training, can improve the self-regulation of attention and emotions, mainly including mindfulness meditation and mantra meditation [14]. Many studies have shown that , Meditation can promote the activity of the left prefrontal cortex and other areas of the brain, and has the potential to treat clinical diseases [15].…”

Section: Contactless Devicesmentioning

confidence: 99%

Application of artificial intelligence in sleep medicine

chen

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Purpose of review ：Sleep is the life instinct of human beings. It is not only of great significance to the physical and mental health of individuals, but also can be used as a natural means of regulating, restoring and enhancing bodily functions. There is a prominent contradiction between health needs and the backward status of daily sleep health management, and there is an urgent need to develop theories and methods from sleep structure conversion mechanism to sleep quality monitoring and intervention. In the past few years, artificial intelligence (AI) technology has rapidly emerged in the field of sleep medicine.Research methods：The purpose of this article is to provide a brief overview of relevant terms, definitions and use cases of artificial intelligence in sleep medicine.Research result: AI has a variety of applications in sleep medicine, including sleep and respiratory event scoring in sleep labs, diagnosis and management of sleep disorders, and population health. Although still in its infancy, there are still challenges that hinder the ubiquity and broad clinical application of AI. Overcoming these challenges will help seamlessly integrate AI into sleep medicine and enhance clinical practice.Research conclusion: AI is a powerful tool in healthcare that can improve patient care, enhance diagnostic capabilities, and enhance the management of sleep disorders. However, before existing machine learning algorithms can be incorporated into sleep clinics, these artificial intelligence devices need to be regulated and standardized.

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“…Table 3. Recent research studies used ES Authors Years ES applications [52] 2016 Evaluation of the performance of sustainable manufacturing [53] 2016 Material handling equipment selection [54] 2019 [55] 2019 Layout designs [56] 2018 Optimization of Design and Manufacturing Process [57] 2019 Shift work [58] 2015 Machine tool selection [59] 2015…”

Section: Is the Expert System Still Promising?mentioning

confidence: 99%

Improvement opportunities of a Simulation/Expert System Approach for Manufacturing System Sizing: A review and proposal

Imen¹,

Masmoudi²,

Elleuch³

et al. 2019

Adv. sci. technol. eng. syst. j.

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Manufacturing System (MS) sizing is a crucial task to complete in order to obtain the desired MS performance and efficiency. It involves selecting the required number of resources from each used type in a given planning horizon. In fact, different approaches coupling simulation/optimization tools have been developed to solve this issue and evaluate the MS performance. One of these approaches is the Simulation Expert System Approach (SESA). Unfortunately, the application domain of this approach is limited in sizing only the production resources (machines and labor) but neglects the material handling system (MHS) components. Moreover, omitting the transferring problem is not viable in the real world due to its importance in each shop floor. Thus, the aim of this paper is to describe the evolution of SESA, then, to check if the simulation optimization tools used in SESA are still relevant. This paper also investigates the importance of incorporating MHS in this approach and finally proposes some improvement opportunities for SESA including the tackling of the MHS fleet sizing problem. In fact, the wide literature review performed in this research indicates that SESA is still a pertinent approach but it must be improved. Therefore, it is expected that SESA improvement opportunities proposed in this work will greatly assist industrialists in enhancing the overall MS performance, providing a significant productivity increase and a minimization of the total production costs.

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Expert system application for prioritizing preventive actions for shift work: shift expert

Cited by 6 publications

References 46 publications

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Application of artificial intelligence in sleep medicine

Improvement opportunities of a Simulation/Expert System Approach for Manufacturing System Sizing: A review and proposal

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