Visualization Analysis of Clinical Decision Support Research Based on Electronic Medical Records (Preprint)

Abstract

Electronic medical records have rich clinical information and knowledge value and are an important data source for clinical decision support systems, but the current utilization of electronic medical record data is low, and there are problems of insufficient structure, lack of standardization, and low quality, which affect the development and application of clinical decision support.

To explore the current research status, research hotspots and frontiers in the field of clinical decision support based on electronic medical records.

In this paper, bibliometric methods were used to obtain relevant literature using the China National Knowledge Infrastructure database and Web of Science core collection database from 2000-2022 as data sources, and electronic medical records and clinical decision support as key search terms. CiteSpace.6.2.R2 software is used to draw a scientific Knowledge graph of author cooperation, national/regional distribution, institutional cooperation, keyword co-occurrence, clustering and time zone map for visual comparative analysis, Python is used to mine and analyze clustering popularity based on the keyword map, so as to understand the differences between domestic and foreign research and provide reference for research and development in this field.

The field of clinical decision support based on electronic medical record data shows rapid development, with the United States and China as the main research countries and strong cooperation between domestic and foreign institutions and authors. The keywords mainly involve electronic medical records, artificial intelligence, machine learning, clinical pathways, data mining, etc., reflecting the core themes and frontier directions in the field.

This paper provides a reference for further promoting the effective use of electronic medical record data and the innovative development of clinical decision-making systems.