ArticleJournal of medical Internet research2024
Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Research Topics and Trends in MIMIC-IV: A Large ICU Database Relevant for Critical Care Nursing.Nursing in critical care · 2026Review
- Development and external validation of a machine learning model for predicting the 28-day mortality risk in patients with sepsis complicated by acute respiratory failure in the ICU.Journal of intensive medicine · 2026Article
- The next frontier in sepsis: connected ICU data for real-world clinical decision making.Intensive care medicine · 2026Review
- The evolving landscape of large language models and non-large language models in health care.npj health systems · 2026Article
- Bridging gaps between scientific research and regulatory decision-making in Europe: roles of academia, risk assessors, and policymakers.Frontiers in toxicology · 2026Article
- Benchmarking Foundation Models with Multimodal Public Electronic Health Records.IEEE journal of biomedical and health informatics · 2025Article
- Enabling inclusive systematic reviews: incorporating preprint articles with large language model-driven evaluations.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Advancing Pediatric Growth Assessment with Machine Learning: Overcoming Challenges in Early Diagnosis and Monitoring.Children (Basel, Switzerland) · 2025Article
- Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review.Journal of medical Internet research · 2025Article
- Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language Models: Simulation Study.Journal of medical Internet research · 2024Article
- Ascle-A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study.Journal of medical Internet research · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntensive care research has predominantly relied on conventional methods like randomized controlled trials. However, the increasing popularity of open-access, free databases in the past decade has opened new avenues for research, offering fresh insights. Leveraging machine learning (ML) techniques enables the analysis of trends in a vast number of studies.
objectiveThis study aims to conduct a comprehensive bibliometric analysis using ML to compare trends and research topics in traditional intensive care unit (ICU) studies and those done with open-access databases (OADs).
methodsWe used ML for the analysis of publications in the Web of Science database in this study. Articles were categorized into "OAD" and "traditional intensive care" (TIC) studies. OAD studies were included in the Medical Information Mart for Intensive Care (MIMIC), eICU Collaborative Research Database (eICU-CRD), Amsterdam University Medical Centers Database (AmsterdamUMCdb), High Time Resolution ICU Dataset (HiRID), and Pediatric Intensive Care database. TIC studies included all other intensive care studies. Uniform manifold approximation and projection was used to visualize the corpus distribution. The BERTopic technique was used to generate 30 topic-unique identification numbers and to categorize topics into 22 topic families.
resultsA total of 227,893 records were extracted. After exclusions, 145,426 articles were identified as TIC and 1301 articles as OAD studies. TIC studies experienced exponential growth over the last 2 decades, culminating in a peak of 16,378 articles in 2021, while OAD studies demonstrated a consistent upsurge since 2018. Sepsis, ventilation-related research, and pediatric intensive care were the most frequently discussed topics. TIC studies exhibited broader coverage than OAD studies, suggesting a more extensive research scope.
conclusionsThis study analyzed ICU research, providing valuable insights from a large number of publications. OAD studies complement TIC studies, focusing on predictive modeling, while TIC studies capture essential qualitative information. Integrating both approaches in a complementary manner is the future direction for ICU research. Additionally, natural language processing techniques offer a transformative alternative for literature review and bibliometric analysis.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.