Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

Madrid, Alfredo; Merino‐Barbancho, Beatriz; Rodríguez-González, Alejandro; Fernández-Gutiérrez, Benjamín; Rodríguez‐Rodríguez, Luis; Menasalvas, Ernestina

doi:10.1101/2022.11.04.22281930

Cited by 3 publications

(3 citation statements)

References 167 publications

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“…This has been made possible thanks to the use of novelty NLP DL architectures (i.e., transformers) for NER. The use of AI techniques by the rheumatology research community has been described elsewhere [40,41]. As reported in previous studies, this SDOH is collected in a very low proportion in the EHR.…”

Section: Discussionmentioning

confidence: 99%

Occupation Recognition and Exploitation in Rheumatology Clinical Notes: Employing Deep Learning Models for Named Entity Recognition and Knowledge Discovery in Electronic Health Records

Madrid-García,

Pérez-Sancristóbal,

Leticia-Leon

et al. 2024

Preprint

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Occupation is considered a Social Determinant of Health (SDOH) and its effects have been studied at multiple levels. Although the inclusion of such data in the Electronic Health Record (EHR) is vital for the provision of clinical care, specially in rheumatology where work disability prevention is essential, occupation information is often either not routinely documented or captured in an unstructured manner within conventional EHR systems. Encouraged by recent advances in natural language processing and deep learning models, we propose the use of novel architectures (i.e., transformers) to detect occupation mentions in rheumatology clinical notes of a tertiary hospital, and to whom those occupations belongs. We also aimed to evaluate the clinical and demographic characteristics that influence the collection of this SDOH; and the association between occupation and patients' diagnosis. Bivariate and multivariate logistic regression analysis were conducted for this purpose. A Spanish pre-trained language model, RoBERTa, fine-tuned with biomedical texts was used to detect occupations. The best model achieved a F1-score of 0.725 identifying occupation mentions. Moreover, highly disabling mechanical pathology diagnoses (i.e., back pain, muscle disorders) were associated with a higher probability of occupation collection. Ultimately, we determined the professions most closely associated with thirteen categories of musculoskeletal disorders.

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Section: Discussionmentioning

confidence: 99%

Occupation Recognition and Exploitation in Rheumatology Clinical Notes: Employing Deep Learning Models for Named Entity Recognition and Knowledge Discovery in Electronic Health Records

Madrid-García,

Pérez-Sancristóbal,

Leticia-Leon

et al. 2024

Preprint

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“…Finally, data collected from Reddit could be used to perform artificial intelligence and natural language processing analyses, such as topic modeling for characterising the latent topics, sentiment analysis for charactersing the polarity of the text, or even graph analysis to determine the hubs and the authoritative Reddit users in each community Madrid-Garcia et al [2023]. Moreover, these data can be used to feed large-language models, enhancing the capabilities of AI-driven chatbots.…”

Section: Rheumatology On Redditmentioning

confidence: 99%

Rheumatology on Reddit: A Descriptive Analysis

García,

Barbancho

2024

Preprint

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Background: Over the past decade, social media data have gained special interest across various sectors, specially in medicine where these data are being utilized for a variety of purposes, including surveillance, pharmacovigilance, or patient recruitment. Among the social platforms, Reddit is one of the most widely used worldwide. The way in which this social network organises information, in specific subreddits, makes it possible to estimate the popularity and relevance of topics, including medical ones. Although there have been multiple studies in rheumatology using these social media, none have detailed the different rheumatology-specific communities that exist within Reddit. Therefore, the objective of this study was twofold: to find rheumatology-related subreddits, and to describe their characteristics, including the number of subscribers, number of users, and temporal behavior among others. Methods: The Reddit search engine, along with a list of 21 commonly used rheumatology terms, was used to identify the candidate subreddits. The subreddit's name, creation date, number of subscribers, public status, size, and activity since May 2023 to May 2024 information was collected. Exclusion criteria were applied to narrow down the results. Only subreddits with over 1000 subscribers, active, with strong affinity and recoverable through Pushshift.io were extracted. Different metrics and statistics were computed to characterise the subreddits identified in the previous step (e.g., number of users who participate in each subreddit, number of messages, thread length, word count per submissions, subreddits activities, most commented threads per subreddit and so on). Continuous variables were summarised using the median and the first and third quartiles (Q1–Q3). Results: Of 83 potential subreddits, 20 met the inclusion criteria. The oldest subreddit was r/Fibromyalgia. The number of subscribers ranged from 2k (r/Behcets) to 70k (r/Fibromyalgia) and the ratio comments/submissions was higher in r/ankylosingspondylitis (12.36). Almost exponential growth is seen for all communities since 2016-2017. r/Fibromyalgia has the highest number of messages per day. Peak activity occurs between 16:00 and 23:00 and the majority of activity was concentrated in the second half of the year. The patient's age was a common concern across subreddits. Another majorn concern was the COVID vaccine and its potential impact on patients with RMDs (i.e., r/mctd, r/costochondritis, r/Sjogrens, r/Uveitis). Some of the most commented posts on r/gout were Ask Me Anything sessions, where professionals respond to patient's questions. This highlights patient's interest in exchanging information with healthcare professionals via social media. Conclusions: Reddit provides a valuable platform for studying patient concerns, needs, and perceptions about their RMDs as evidenced by the number of subscribers, the diversity of communities, and the extent of their activity and size. This paper contributes to understand how rheumatic and musculoskeletal communities utilize Reddit to discuss health-related topics, potentially informing better patient support and engagement strategies in digital spaces.

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“…In rheumatology, the use of AI and LLM, such chatbots, is gaining relevance [15]. A query conducted on July 10 th in PubMed, "Rheumatology AND ChatGPT" produced twelve results.…”

Section: Introductionmentioning

confidence: 99%

Harnessing ChatGPT and GPT-4 for Evaluating the Rheumatology Questions of the Spanish Access Exam to Specialized Medical Training

Madrid

Rosales-Rosado

Freites

et al. 2023

Preprint

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The emergence of Large Language Models (LLM) with remarkable performance such as ChatGPT and GPT-4, has led to an unprecedented uptake in the population. One of their most promising and studied applications concerns education due to their ability to understand and generate human-like text, creating a multitude of opportunities for enhancing educational practices and outcomes. The objective of this study is two-fold: to assess the accuracy of ChatGPT/GPT-4 in answering rheumatology questions from the access exam to specialized medical training in Spain (MIR), and to evaluate the medical reasoning followed by these LLM to answer those questions. A dataset, RheumaMIR, of 145 rheumatology-related questions, extracted from the exams held between 2010 and 2023, was created for that purpose, used as a prompt for the LLM, and was publicly distributed. Six rheumatologists with clinical and teaching experience evaluated the clinical reasoning of the chatbots using a 5-point Likert scale and their degree of agreement was analyzed. The association between variables that could influence the models' accuracy (i.e., year of the exam question, disease addressed, type of question and genre) was studied. ChatGPT demonstrated a high level of performance in both accuracy, 66.43%, and clinical reasoning, median (Q1-Q3), 4.5 (2.33-4.67). However, GPT-4 showed better performance with an accuracy score of 93.71% and a median clinical reasoning value of 4.67 (4.5-4.83). These findings suggest that LLM may serve as valuable tools in rheumatology education, aiding in exam preparation and supplementing traditional teaching methods.

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Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

Cited by 3 publications

References 167 publications

Occupation Recognition and Exploitation in Rheumatology Clinical Notes: Employing Deep Learning Models for Named Entity Recognition and Knowledge Discovery in Electronic Health Records

Occupation Recognition and Exploitation in Rheumatology Clinical Notes: Employing Deep Learning Models for Named Entity Recognition and Knowledge Discovery in Electronic Health Records

Rheumatology on Reddit: A Descriptive Analysis

Harnessing ChatGPT and GPT-4 for Evaluating the Rheumatology Questions of the Spanish Access Exam to Specialized Medical Training

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