Health intelligence

Shaban-Nejad, Arash; Kamaleswaran, Rishikesan; Shin, Eun Kyong; Akbilgiç, Oğuz

doi:10.1016/b978-0-12-816034-3.00006-7

Cited by 3 publications

(3 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The intricacies of this problem could potentially be addressed through the application of modern artificial intelligence (AI) techniques, particularly ML. It is hoped that these techniques can effectively relate key factors and provide insights, if not transform the perspective, for addressing such challenges [47].…”

Section: Autism Spectrum Disorder and Gut Microbiota Compositionmentioning

confidence: 99%

Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition

Olaguez-Gonzalez,

Chairez,

Breton-Deval

et al. 2023

Biomedicines

View full text Add to dashboard Cite

The application of machine learning (ML) techniques stands as a reliable method for aiding in the diagnosis of complex diseases. Recent studies have related the composition of the gut microbiota to the presence of autism spectrum disorder (ASD), but until now, the results have been mostly contradictory. This work proposes using machine learning to study the gut microbiome composition and its role in the early diagnosis of ASD. We applied support vector machines (SVMs), artificial neural networks (ANNs), and random forest (RF) algorithms to classify subjects as neurotypical (NT) or having ASD, using published data on gut microbiome composition. Naive Bayes, k-nearest neighbors, ensemble learning, logistic regression, linear regression, and decision trees were also trained and validated; however, the ones presented showed the best performance and interpretability. All the ML methods were developed using the SAS Viya software platform. The microbiome’s composition was determined using 16S rRNA sequencing technology. The application of ML yielded a classification accuracy as high as 90%, with a sensitivity of 96.97% and specificity reaching 85.29%. In the case of the ANN model, no errors occurred when classifying NT subjects from the first dataset, indicating a significant classification outcome compared to traditional tests and data-based approaches. This approach was repeated with two datasets, one from the USA and the other from China, resulting in similar findings. The main predictors in the obtained models differ between the analyzed datasets. The most important predictors identified from the analyzed datasets are Bacteroides, Lachnospira, Anaerobutyricum, and Ruminococcus torques. Notably, among the predictors in each model, there is the presence of bacteria that are usually considered insignificant in the microbiome’s composition due to their low relative abundance. This outcome reinforces the conventional understanding of the microbiome’s influence on ASD development, where an imbalance in the composition of the microbiota can lead to disrupted host–microbiota homeostasis. Considering that several previous studies focused on the most abundant genera and neglected smaller (and frequently not statistically significant) microbial communities, the impact of such communities has been poorly analyzed. The ML-based models suggest that more research should focus on these less abundant microbes. A novel hypothesis explains the contradictory results in this field and advocates for more in-depth research to be conducted on variables that may not exhibit statistical significance. The obtained results seem to contribute to an explanation of the contradictory findings regarding ASD and its relation with gut microbiota composition. While some research correlates higher ratios of Bacillota/Bacteroidota, others find the opposite. These discrepancies are closely linked to the minority organisms in the microbiome’s composition, which may differ between populations but share similar metabolic functions. Therefore, the ratios of Bacillota/Bacteroidota regarding ASD may not be determinants in the manifestation of ASD.

show abstract

Section: Autism Spectrum Disorder and Gut Microbiota Compositionmentioning

confidence: 99%

Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition

Olaguez-Gonzalez,

Chairez,

Breton-Deval

et al. 2023

Biomedicines

View full text Add to dashboard Cite

show abstract

“…A su vez, los asistentes robóticos se han adoptado en diversos campos de servicios, como los servicios hoteleros [17]. Igualmente, la IA posibilita por medio de máquinas la captura y procesamiento de grandes volúmenes de múltiples datos, con el objetivo de resolver situaciones complejas de manera eficiente [18].…”

Section: Nuevas Tecnologías Aplicadas a La Industria De La Hospitalidadunclassified

Digital entrepreneurship in tourism as an engine of economic reactivation in Colombia: a theoretical approach.

Puerta Guardo,

Cantillo Orozco,

Vergara Gómez

et al. 2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

View full text Add to dashboard Cite

Globalization, the revolution of new information technologies and digital entrepreneurship, has triggered a growth in the tourism sector, expanding to new agents and regions. This situation motivates to conduct research on the challenges and changes in the industry, which is why the purpose of this paper is to analyze the importance of digital entrepreneurship leveraged on technologies and innovation, and its relevance in the economic revival in the tourism industry in Colombia. For its scope (1), we start from a bibliometric analysis supported by the tools VOSviewer 1.6.19, Biblioshiny R and Vantage Point, of 364 documents indexed in Scopus and Web of science, based on the equation (("entrepreneurship*" OR "digital entrepreneurship*" OR "artificial intelligence") AND ("Tourism* Entrepreneurship*" OR "Tourism*")) [2] As a complement, we analyzed technological tools used in the sector, its value proposition and business model. The results show an average growing trend of 43.32%, with the highest scientific production for the year 2022 (121 articles). Similarly, the United Kingdom and China are the countries with the highest level of production and collaborative work. Entrepreneurship, Tourism and Innovation are ranked as Driving Themes, reflecting their importance in recent research. Finally, innovation is a relevant factor in the processes of digital entrepreneurship, influencing in a disruptive way the growth of the tourism industry.

show abstract

“…AI is considered a revolutionary system that can mimic the intelligence of a human but at a much higher capacity (Katiyar and Katiyar, 2021;Shi et al, 2022). AI has been implemented in various industries to address complex optimization problems in a rapid and efficient manner (Shaban-Nejad et al, 2020;Shi et al, 2022). The applications of AI in TEA can be found in the form of its subfield, namely, GA, ML, and ANN, which were elaborated in the next two subsections.…”

Section: Artificial Intelligent (Ai)mentioning

confidence: 99%

Future era of techno-economic analysis: Insights from review

et al. 2022

View full text Add to dashboard Cite

Techno-economic analysis (TEA) has been considered an important tool to evaluate the economic performance of industrial processes. Recently, the application of TEA has been observed to have exponential growth due to the increasing competition among businesses across various industries. Thus, this review presents a deliberate overview of TEA to inculcate the importance and relevance of TEA. To further support the aforementioned points, this review article starts with a bibliometric analysis to evaluate the applicability of TEA within the research community. Conventional TEA is widely known to be conducted via software modeling (i.e., Python, AMIS, MATLAB, Aspen HYSYS, Aspen Plus, HOMER Pro, FORTRAN, R, SysML and Microsoft Excel) without involving any correlation or optimization between the process and economic performance. Apart from that, due to the arrival of the industrial revolution (IR) 4.0, industrial processes are being revolutionized into smart industries. Thus, to retain the integrity of TEA, a similar evolution to smart industries is deemed necessary. Studies have begun to incorporate data-driven technologies (i.e., artificial intelligence (AI) and blockchain) into TEA to effectively optimize both processes and economic parameters simultaneously. With this, this review explores the integration of data-driven technologies in the TEA framework. From literature reviews, it was found that genetic algorithm (GA) is the most applied data-driven technology in TEA, while the applications of blockchain, machine learning (ML), and artificial neural network (ANN) in TEA are still considerably scarce. Not to mention other advanced technologies, such as cyber-physical systems (CPS), IoT, cloud computing, big data analytics, digital twin (DT), and metaverse are yet to be incorporated into the existing TEA. The inclusion of set-up costs for the aforementioned technologies is also crucial for accurate TEA representation of smart industries deployment. Overall, this review serves as a reference note for future process engineers and industry stakeholders who wish to perform relevant TEA, which is capable to cover the new state-of-art elements under the new modern era.

show abstract

Health intelligence

Cited by 3 publications

References 83 publications

Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition

Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition

Digital entrepreneurship in tourism as an engine of economic reactivation in Colombia: a theoretical approach.

Future era of techno-economic analysis: Insights from review

Contact Info

Product

Resources

About