Dataset Information

Autoimmune Disease Classification Based on PubMed Text Mining.

ABSTRACT: Autoimmune diseases (AIDs) are often co-associated, and about 25% of patients with one AID tend to develop other comorbid AIDs. Here, we employ the power of datamining to predict the comorbidity of AIDs based on their normalized co-citation in PubMed. First, we validate our technique in a test dataset using earlier-reported comorbidities of seven knowns AIDs. Notably, the prediction correlates well with comorbidity (R = 0.91) and validates our methodology. Then, we predict the association of 100 AIDs and classify them using principal component analysis. Our results are helpful in classifying AIDs into one of the following systems: (1) gastrointestinal, (2) neuronal, (3) eye, (4) cutaneous, (5) musculoskeletal, (6) kidneys and lungs, (7) cardiovascular, (8) hematopoietic, (9) endocrine, and (10) multiple. Our classification agrees with experimentally based taxonomy and ranks AID according to affected systems and gender. Some AIDs are unclassified and do not associate well with other AIDs. Interestingly, Alzheimer's disease correlates well with other AIDs such as multiple sclerosis. Finally, our results generate a network classification of autoimmune diseases based on PubMed text mining and help map this medical universe. Our results are expected to assist healthcare workers in diagnosing comorbidity in patients with an autoimmune disease, and to help researchers in identifying common genetic, environmental, and autoimmune mechanisms.

SUBMITTER: Samuels H

PROVIDER: S-EPMC9369164 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Autoimmune Disease Classification Based on PubMed Text Mining.

Samuels Hadas H Malov Malki M Saha Detroja Trishna T Ben Zaken Karin K Bloch Naamah N Gal-Tanamy Meital M Avni Orly O Polis Baruh B Samson Abraham O AO

Journal of clinical medicine 20220726 15

Autoimmune diseases (AIDs) are often co-associated, and about 25% of patients with one AID tend to develop other comorbid AIDs. Here, we employ the power of datamining to predict the comorbidity of AIDs based on their normalized co-citation in PubMed. First, we validate our technique in a test dataset using earlier-reported comorbidities of seven knowns AIDs. Notably, the prediction correlates well with comorbidity (R = 0.91) and validates our methodology. Then, we predict the association of 100 ...[more]

PMID: 35893435

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Project description:AimMany therapeutic options for inflammatory bowel disease (IBD) emerged during the last 2 decades, along with the rise in disease prevalence and incidence. We aimed at assessing the published literature on different treatment options in that period. Special attention was attributed to specific medication mechanisms and geographic diversity.Materials and methodsWe have queried PubMed for all available IBD-related entries published during 2000-2020. The following data were extracted for each entry: PubMed unique article ID (PMID), title, publishing journal, abstract text, keywords (if any), and authors' affiliations. Two gastrointestinal specialists decided in consensus on a list of terms to classify entries. The terms belonged to five treatment groups: medical, surgical, dietary, microbiome manipulation, and complementary medicine. The medical and complementary medicine groups were further sub-classified. Annual trends of publications for the years 2000-2020 were plotted for different treatment types. The slopes of publication trends were calculated by fitting regression lines to the annual number of publications.ResultsOverall, 77,505 IBD entries were published between 2000 and 2020. Medical treatment showed the highest number of total publications 21,540/77,505 (27.8%), followed by surgical 7605/77,505 (9.8%), microbiome research 5260/77,505 (6.8%), dietary 4819/77,505 (6.2%), and complementary medicine treatment 762/77,505 (1.0%). Interestingly, since 2012 there is a steep rise in microbiome publications that outnumbered surgery in the last 2 years. Trend analysis of medical treatment showed that biologics had the steepest slope (57.5, p < 0.001), followed by immunomodulators (4.9, p < 0.001), small molecules (3.9, p < 0.001), and 5-ASA (3.8, p < 0.001).ConclusionAccording to our high-level publications trend analysis, the past 2 decades certainly deserve the reference as the "biologic era", as publications regarding biological therapy outnumbered all other treatment options. Interestingly, though very popular among patients, complementary medicine was not studied with correlation to its' acceptance among patients.

Project description:BackgroundStudies using Taiwan's National Health Insurance (NHI) claims data have expanded rapidly both in quantity and quality during the first decade following the first study published in 2000. However, some of these studies were criticized for being merely data-dredging studies rather than hypothesis-driven. In addition, the use of claims data without the explicit authorization from individual patients has incurred litigation.ObjectiveThis study aimed to investigate whether the research output during the second decade after the release of the NHI claims database continues growing, to explore how the emergence of open access mega journals (OAMJs) and lawsuit against the use of this database affect the research topics and publication volume and to discuss the underlying reasons.MethodsPubMed was used to locate publications based on NHI claims data between 1996 and 2017. Concept extraction using MetaMap was employed to mine research topics from article titles. Research trends were analyzed from various aspects, including publication amount, journals, research topics and types, and cooperation between authors.ResultsA total of 4473 articles were identified. A rapid growth in publications was witnessed from 2000 to 2015, followed by a plateau. Diabetes, stroke, and dementia were the top 3 most popular research topics whereas statin therapy, metformin, and Chinese herbal medicine were the most investigated interventions. Approximately one-third of the articles were published in open access journals. Studies with two or more medical conditions, but without any intervention, were the most common study type. Studies of this type tended to be contributed by prolific authors and published in OAMJs.ConclusionsThe growth in publication volume during the second decade after the release of the NHI claims database was different from that during the first decade. OAMJs appeared to provide fertile soil for the rapid growth of research based on NHI claims data, in particular for those studies with two or medical conditions in the article title. A halt in the growth of publication volume was observed after the use of NHI claims data for research purposes had been restricted in response to legal controversy. More efforts are needed to improve the impact of knowledge gained from NHI claims data on medical decisions and policy making.

Dataset Information

Autoimmune Disease Classification Based on PubMed Text Mining.

Publications

Autoimmune Disease Classification Based on PubMed Text Mining.

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