This paper is in the following e-collection/theme issue:

Digital Science, Open Science (79) Data Science (276) Epublishing and Open Access (86) Artificial Intelligence (1400) Big Data (241) Machine Learning (1485) Intensive Care Unit (ICU) (113)

Published on in Vol 26 (2024)

This is a member publication of National University of Singapore

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48330, first published .
Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study

Authors of this article:

Yuhe Ke1 Author Orcid Image ;   Rui Yang2 Author Orcid Image ;   Nan Liu2 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Yang R, Zeng Q, You K, Qiao Y, Huang L, Hsieh C, Rosand B, Goldwasser J, Dave A, Keenan T, Ke Y, Hong C, Liu N, Chew E, Radev D, Lu Z, Xu H, Chen Q, Li I. Ascle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study. Journal of Medical Internet Research 2024;26:e60601 View
  2. Ke Y, Yang R, Lie S, Lim T, Ning Y, Li I, Abdullah H, Ting D, Liu N. Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language Models: Simulation Study. Journal of Medical Internet Research 2024;26:e59439 View