Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review

Abstract

Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

Detection of primary Sjögren’s syndrome in primary care: developing a classification model with the use of routine healthcare data and machine learning

“…To our knowledge, this is the first study which looks at the potential of using machine learning to classify patients with pSS in primary care using EHR data from the GP. Previous machine learning studies in pSS have focused on 1) determinants of diagnoses based on hospital EHR data [28], 2) pathogenesis based treatments [29] or 3) identifying pSS subtypes [6]. This study resembles the first exploration of the potential of machine learning methods for classifying patients with pSS in primary care.…”

Artificial Intelligence in Conversational Agents

“…On one hand, if AI is granted human-equivalent rights and extensive access to decision-making processes, this could lead to AI taking control and consequently result in human beings becoming secondary [28], and this has raised concerns in terms of ethics, transparency and accountability [15,50]. On the other hand, AI has the potential to revolutionize society and to create a utopia in which human beings could cure diseases [16], AI could supplement human contributions [22], and individuals could spend more time pursuing personal growth and individual passions. Today, AI has been used to automate many parts of our lives, for instance in autonomous vehicles (e.g., Tesla), robots for home cleaning (e.g., Xiaomi Roborock), digital nurses that monitor the condition of a patient (e.g., Sensely), and voice-controlled speakers (e.g., Apple Homepod) to manage home accessories (e.g., to switch on lights or play music).…”

New Visual Expression of Anime Film Based on Artificial Intelligence and Machine Learning Technology

“…The visual performance of the film, combined with the informatization and intelligent evolution of the whole society, needs to correspond to the times and continuously improve the visual impact of film works [5,6]. Therefore, it is very important to innovate the visual performance of movies, explore new ways of visual performance of movies, and meet the characteristics of the times and industry needs [7,8].…”

“…Therefore, Foulquier et al studied a comparison of a deep learning architecture called deep neural network (DNN) with classical random forest (RF) machine learning algorithms for malware classification. They studied the performance of classic RF and DNN with 2-, 4-, and 7-layer architectures with 4 different feature sets and found that the classic RF accuracy is better than DNN regardless of the feature input [7]. The research of artificial intelligence has become a hot spot in academia and industry.…”