An Artificial Intelligence Based Approach Towards Inclusive Healthcare Provisioning in Society 5.0: A Perspective on Brain Disorder

Mamun, Shamim Al; Kaiser, M. Shamim; Mahmud, Mufti

doi:10.1007/978-3-030-86993-9_15

Cited by 29 publications

(6 citation statements)

References 33 publications

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“…Manufacturing 2020 [28] 16 Smart state net scheme Smart cities 2020 [29] 17 Teleoperation system Generic 2021 [30] 18 Secure hash algorithm (SHA)-256 accelerator Generic 2021 [31] 19 AI-based communication-as-a-service Generic 2021 [32] 20 AI-based heart monitoring system Healthcare 2021 [33] 21 Intelligent health service Healthcare 2021 [34] 22 AIoT based picking algorithm eCommerce 2021 [35] 23 Robotics and digital platform (IoT) Healthcare 2021 [21] 24 Reference architecture for framework for MNCRP Generic 2021 [36] 25 530-mW multicore blockchain accelerator Generic 2021 [37] (continued) 27 Flexible/modular manufacturing system architecture Manufacturing 2021 [39] 28 Digital twin for smart cities Smart cities 2021 [40] 29 A generalized framework for tactile internet Generic 2021 [41] 30 Shiojiri environmental data compilation platform and IoT network Generic 2021 [42] 31 SMARTAGE platform Healthcare 2022 [12] 32 Delay-tolerant public blockchain network in healthcare systems…”

Section: Digital Architectures Under S50mentioning

confidence: 99%

Digital Architectures Under Society 5.0: An Enterprise Architecture Perspective

Piest

Masuda

Iacob

2023

Lecture Notes in Business Information Processing

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As Japan's vision regarding Society 5.0 is unfolding, several digital architectures are (being) designed and constructed. Although Society 5.0 is extensively researched and its application is illustrated in several domains, little research is conducted regarding digital architectures under Society 5.0. The aim of this paper is to identify and analyze digital architectures that are (being) designed and constructed under Society 5.0 from an enterprise architecture perspective. Extending the results of related studies, a systematic literature review is conducted to find out which enterprise architecture frameworks and principles are utilized to create digital architectures under Society 5.0. The initial search in three scientific databases resulted in a sample of 857 documents. After removing duplicates and screening based on abstracts, 116 documents have been analyzed. This revealed a variety of 37 digital architectures in different domains. The main finding is that the analyzed digital architectures mainly use conceptual and system representations, make little use of established enterprise architecture frameworks, are not based on explicit principles, and do not refer to a reference architecture for Society 5.0. This paper presents an enterprise architecture perspective on Society 5.0 and is considered of value for scholars and enterprise architects that are interested in digital architectures under Society 5.0. This study is limited to scientific databases and selected published articles. Empirical research is required to validate the findings. As the interest in Society 5.0 is increasing, it is recommended to develop reference architectures and guiding principles to support digital transformation processes. Future research can contribute to develop prescriptive design knowledge and supporting methods for designing, constructing, and evaluating digital architectures under Society 5.0.

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Section: Digital Architectures Under S50mentioning

confidence: 99%

Digital Architectures Under Society 5.0: An Enterprise Architecture Perspective

Piest

Masuda

Iacob

2023

Lecture Notes in Business Information Processing

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“…Modern medicine faces difficulty utilizing the extensive knowledge base required to diagnose and treat complicated mental health issues. With the abundance availability of structured and unstructured data, ML is increasingly deployed to manage multiple mental health conditions [ 27 ], such as epilepsy [ 50 ], and Alzheimer [ 51 ]. ML is a collection of algorithms that infer meaningful patterns from data without requiring human intervention [ 52 ].…”

Section: Literature Surveymentioning

confidence: 99%

“…Additionally, there is growing interest in applying ML algorithms to the field of brain disorders and mental health [ 22 , 23 ]. The application of ML on the large clinical data [ 24 ] can ensure trustworthy and efficient healthcare decisions benefiting both patients [ 25 , 26 ] and providers [ 27 ]. Personalization facilitated by ML has become engrained in our daily interactions with a variety of digital systems [ 28 ], including e-commerce [ 29 ], movie recommendations, exercise advice [ 30 ], and therapy recommendations [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

et al. 2022

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Autism spectrum is a brain development condition that impairs an individual’s capacity to communicate socially and manifests through strict routines and obsessive–compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81–84%, with a normalized discounted cumulative gain of 79–81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models’ treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.

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“…Additionally, there is growing interest in applying ML algorithms to the field of brain disorders and mental health [22,23]. The application of ML on the large clinical data [24] can ensure trustworthy and efficient healthcare decisions benefiting both patients [25,26] and providers [27]. Personalization facilitated by ML has become engrained in our daily interactions with a variety of digital systems [28], including e-commerce [29], movie recommendations, exercise advice [30], and therapy recommendations [31].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-based ABA treatmentrecommendation and personalization for AutismSpectrum Disorder: An exploratory study

Kohli

Kar

et al. 2022

Preprint

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Autism spectrum is a brain development conditionthat impairs an individual’s capacity tocommunicate socially and manifests itself throughstrict routines and obsessive-compulsive behavior.Applied behavior analysis (ABA) is thegold-standard treatment for autism spectrumdisorder (ASD). However, as the number of ASDcases increases, there is a substantial shortage oflicensed ABA practitioners, limiting the timelyformulation, revision, and implementation oftreatment plans and goals. Additionally, thesubjectivity of the clinician and a lack ofdata-driven decision-making affect treatmentquality. We address these obstacles by applying twomachine learning algorithms to recommend andpersonalize ABA treatment goals for 29 studyparticipants with ASD. The patient similarity andcollaborative filtering methods predicted ABAtreatment with an average accuracy of 81-84percent, with a normalized discounted cumulativegain of 79-81 percent (NDCG) compared toclinician-prepared ABA treatmentrecommendations. Additionally, we assess the twomodels’ treatment efficacy (TE) by measuring thepercentage of recommended treatment goalsmastered by the study participants. The proposedtreatment recommendation and personalizationstrategy are generalizable to other interventionmethods in addition to ABA and for other braindisorders. This study has been registered as aclinical trial on November 5, 2020 with trailregistration number CTRI/2020/11/028933

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An Artificial Intelligence Based Approach Towards Inclusive Healthcare Provisioning in Society 5.0: A Perspective on Brain Disorder

Cited by 29 publications

References 33 publications

Digital Architectures Under Society 5.0: An Enterprise Architecture Perspective

Digital Architectures Under Society 5.0: An Enterprise Architecture Perspective

Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

Machine Learning-based ABA treatmentrecommendation and personalization for AutismSpectrum Disorder: An exploratory study

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