Bibliometric Analysis of Clinical Decision Support Systems

Abstract

Clinical decision support systems are computer systems that help decision-makers make effective and efficient decisions in the diagnosis and treatment of diseases, patient care, and health institution management. In this study, descriptive and bibliometric analyses were carried out of studies on clinical decision support systems conducted between 2016 and 2021 in journals included in the Web of Science database. In the first stage, the distribution of the articles was determined according to the publication year, country, institution, journal and citation numbers within the scope of descriptive analysis. In the second stage, within the scope of bibliometric analysis, co-author-institution, co-citation-author, cocitation-country and common keyword analysis of the articles were made. As a result of the research, 8052 articles on clinical decision support systems were reached. It was observed that the country with the most studies on this subject are the United States and the institution is Harvard University. It was seen that the journal with the highest number of articles was PLOS ONE. In addition, co-citations, institutional and country collaborations between authors are presented with visual maps. The purpose of this study is to present the studies on clinical decision support systems in a systematic framework and to provide a broad perspective to the researchers and field experts working on this subject.

Minimal algorithms for knowledge representation in clinical decision support systems research: a theoretical and empirical analysis

“…Theorem 3.1. (The equivalency theorem for M (DT ) and M (S) ) A matricial representation of a decision table, M (DT ) , given by equation (1), is equivalent to a matricial representation of a statistical machine learning dataset, M (S) , given by equation ( 4), if M (DT ) is transposed, such as…”

“…The algorithm (1) shows that paths can be readly obtained from data arranged in terms of equation ( 6), providing a straightforward computational representation of G and, by definition, the relation RDT for a given decision table . Algorithm 1 The Load data as graph paths algorithm 1: p ← inputted string for CSV file path 2: q := string code for line break G ← G ∪ {t} 11: end for 12: return G In the context of algorithm (1), read is a method for obtaining data in computer physical memmory through a pointer object, and split is a method to split a string at specific positions (given by c). These methods are commonly provided on most of currently used programming languages for artificial intelligence implementation (e.g., python).…”

“…In this context, clinical decision support systems (CDSS) have transvased academic circles to daily life, with large operational systems affecting thousand of patients and professionals per day [7]. Today, CDSS is perceived by many authors as one of the most promising concept among emergent technologies in healthcare [5, 2, 7, 1].…”

A pipeline for tabular dataset formation from unstructured data provided by ACR Appropriateness Criteria guidelines

“…More over, the interested contents usually appear in many different places, composing different document sections. An example can be viwed at figure (1).…”

“…Here, our contents of interest comprises the Variant texts and the tables for Appropriateness Criteria (see figure (1)). Also, the filename presents the name of the guideline specific group, providing another piece of interested data for our purposes.…”