| Literature DB >> 35665364 |
Kemin Li1,2, Chenzhe Feng3,4, Haolin Chen5, Yeqian Feng3, Jingnan Li1,2.
Abstract
Background: Inflammatory bowel disease (IBD) is a continuously increasing and worldwide disease, and the number of publications of IBD has been expanding in the past 10 years. The purpose of this study is to analyze the published articles of IBD in the past decade via machine learning and text analysis and get a more comprehensive understanding of the research trends and changes in IBD in the past 10 years. Method: In November 2021, we downloaded the published articles related to IBD in PubMed for the past 10 years (2012-2021). We utilized Python to extract the title, publication date, MeSH terms, and abstract from the metadata of each publication for bibliometric assessment. Latent Dirichlet allocation (LDA) was used to the abstracts to identify publications' research topics with greater specificity. Result: We finally identified and analyzed 34,458 publications in total. We found that publications in the last 10 years were mainly focused on treatment and mechanism. Among them, publications on biological agents and Gastrointestinal Microbiome have a significant advantage in terms of volume and rate of publications. In addition, publications related to IBD and coronavirus disease 2019 (COVID-19) have increased sharply since the outbreak of the worldwide pandemic caused by novel β-coronavirus in 2019. However, researchers seem to pay less attention to the nutritional and psychological status of patients with IBD.Entities:
Keywords: bibliometric; coronavirus disease 2019; inflammatory bowel disease; machine learning; publication analysis
Year: 2022 PMID: 35665364 PMCID: PMC9160461 DOI: 10.3389/fmed.2022.880553
Source DB: PubMed Journal: Front Med (Lausanne) ISSN: 2296-858X
Figure 1PubMed search results: articles per year.
Overall ranking of research foci in the past 10 years.
|
|
|
|
|---|---|---|
| 1 | Treatment outcome | 4,410 |
| 2 | Intestinal mucosa | 3,562 |
| 3 | Risk factors | 3,142 |
| 4 | Tumor necrosis factor-alpha | 2,967 |
| 5 | Infliximab | 2,770 |
| 6 | Severity of illness index | 2,713 |
| 7 | Mice | 2,704 |
| 8 | Gastrointestinal agents | 2,273 |
| 9 | Immunosuppressive agents | 2,228 |
| 10 | Biomarkers | 2,197 |
| 11 | Anti-inflammatory agents | 2,157 |
| 12 | Disease models, animal | 1,858 |
| 13 | Inflammation | 1,837 |
| 14 | Prognosis | 1,799 |
| 15 | Antibodies, monoclonal | 1,783 |
| 16 | Colonoscopy | 1,741 |
| 17 | Feces | 1,573 |
| 18 | Gastrointestinal microbiome | 1,477 |
| 19 | Time factors | 1,393 |
| 20 | Adalimumab | 1,389 |
Figure 2Annual publication of IBD-related literature, divided by age group.
Top 15 research foci in each 2-years period examined.
|
|
|
|
|
|
|
|---|---|---|---|---|---|
| 1 | Treatment outcome | Animals | Treatment outcome | Treatment outcome | Treatment outcome |
| 2 | Antibodies, monoclonal | Treatment outcome | Intestinal mucosa | Intestinal mucosa | Intestinal mucosa |
| 3 | Intestinal mucosa | Risk factors | Risk factors | Mice | Mice |
| 4 | Risk factors | Intestinal mucosa | Tumor necrosis factor-alpha | Tumor necrosis factor-alpha | Gastrointestinal microbiome |
| 5 | Immunosuppressive agents | Tumor necrosis factor-alpha | Infliximab | Risk factors | Infliximab |
| 6 | Tumor necrosis factor-alpha | Severity of illness index | Severity of illness index | Gastrointestinal agents | Risk factors |
| 7 | Infliximab | Immunosuppressive agents | Gastrointestinal agents | Severity of illness index | Severity of illness index |
| 8 | Anti-inflammatory agents | Infliximab | Mice | Infliximab | Disease models, animal |
| 9 | Severity of illness index | Antibodies, monoclonal | Biomarkers | Biomarkers | Inflammation |
| 10 | Prognosis | Anti-inflammatory agents | Immunosuppressive agents | Disease models, animal | Tumor necrosis factor-alpha |
| 11 | Mice | Prognosis | Anti-inflammatory agents | Gastrointestinal microbiome | Biomarkers |
| 12 | Anti-inflammatory agents, non-steroidal | Gastrointestinal agents | Colonoscopy | Inflammation | COVID-19 |
| 13 | Biomarkers | Mice | Gastrointestinal microbiome | Anti-inflammatory agents | Gastrointestinal agents |
| 14 | Antibodies, monoclonal, humanized | Biomarkers | Feces | Prognosis | Anti-inflammatory agents |
| 15 | Genetic predisposition to disease | Time factors | Remission induction | Immunosuppressive agents | SARS-CoV-2 |
Top 10 research foci related to treatment in each 2-years period.
|
|
|
|
|
|
|
|---|---|---|---|---|---|
| 1 | Antibodies, monoclonal | Immunosuppressive agents | Infliximab | Gastrointestinal agents | Infliximab |
| 2 | Immunosuppressive agents | Infliximab | Gastrointestinal agents | Infliximab | Gastrointestinal agents |
| 3 | Infliximab | Antibodies, monoclonal | Immunosuppressive agents | Anti-inflammatory agents | Anti-inflammatory agents |
| 4 | Anti-inflammatory agents | Anti-inflammatory agents | Anti-inflammatory agents | Immunosuppressive agents | Remission induction |
| 5 | Anti-inflammatory agents, non-steroidal | Gastrointestinal agents | Remission induction | Remission induction | Tumor necrosis factor inhibitors |
| 6 | Antibodies, monoclonal, humanized | Anti-inflammatory agents, non-steroidal | Adalimumab | Antibodies, monoclonal, humanized | Immunosuppressive agents |
| 7 | Gastrointestinal agents | Remission induction | Colectomy | Adalimumab | Adalimumab |
| 8 | Remission induction | Antibodies, monoclonal, humanized | Antibodies, monoclonal | Colectomy | Antibodies, monoclonal, humanized |
| 9 | Postoperative complications | Adalimumab | Postoperative complications | Postoperative complications | Postoperative complications |
| 10 | Adalimumab | Colectomy | Antibodies, monoclonal, humanized | Antibodies, monoclonal | Colectomy |
Figure 3Comparison between the total amount of Treatment Outcome and Risk Facters, and some MeSH terms related to nutritional status and psychological assessment.
Figure 4LDA analysis: the top 10 topic with the highest average number of publications per year.
Figure 5LDA research topic cluster network: inter- and intra-relationships. The orange cluster represents the “Health Management” cluster, the purple cluster represents he “Diagnose and Treatment” cluster, and the green cluster represents the “Basic Research” cluster.