Machine Learning-Based Disease Diagnosis:A Bibliometric Analysis
- URL: http://arxiv.org/abs/2201.02755v1
- Date: Sat, 8 Jan 2022 04:04:51 GMT
- Title: Machine Learning-Based Disease Diagnosis:A Bibliometric Analysis
- Authors: Md Manjurul Ahsan, Zahed Siddique
- Abstract summary: Machine Learning (ML) has garnered considerable attention from researchers and practitioners as a new and adaptable tool for disease diagnosis.
With the advancement of ML and the proliferation of papers and research in this field, a complete examination of Machine Learning-Based Disease Diagnosis (MLBDD) is required.
This article comprehensively studies MLBDD papers from 2012 to 2021.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Machine Learning (ML) has garnered considerable attention from researchers
and practitioners as a new and adaptable tool for disease diagnosis. With the
advancement of ML and the proliferation of papers and research in this field, a
complete examination of Machine Learning-Based Disease Diagnosis (MLBDD) is
required. From a bibliometrics standpoint, this article comprehensively studies
MLBDD papers from 2012 to 2021. Consequently, with particular keywords, 1710
papers with associate information have been extracted from the Scopus and Web
of Science (WOS) database and integrated into the excel datasheet for further
analysis. First, we examine the publication structures based on yearly
publications and the most productive countries/regions, institutions, and
authors. Second, the co-citation networks of countries/regions, institutions,
authors, and articles are visualized using R-studio software. They are further
examined in terms of citation structure and the most influential ones. This
article gives an overview of MLBDD for researchers interested in the subject
and conducts a thorough and complete study of MLBDD for those interested in
conducting more research in this field.
Related papers
- Decoding MIE: A Novel Dataset Approach Using Topic Extraction and Affiliation Parsing [0.0]
This study introduces a novel dataset derived from the Medical Informatics Europe (MIE) Conference proceedings.
We extracted and processed metadata and abstract from 4,606 articles published in the "Studies in Health Technology and Informatics" journal series.
arXiv Detail & Related papers (2024-10-06T19:34:23Z) - Diagnostic Reasoning in Natural Language: Computational Model and Application [68.47402386668846]
We investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR)
We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models.
We use the resulting dataset to investigate the human decision-making process in NL-DAR.
arXiv Detail & Related papers (2024-09-09T06:55:37Z) - Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML)
This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature.
The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
arXiv Detail & Related papers (2024-07-17T20:01:21Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews.
The newly emerging AI-generated literature reviews are also appraised.
This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - A Comprehensive Study of Groundbreaking Machine Learning Research:
Analyzing highly cited and impactful publications across six decades [1.6442870218029522]
Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields.
It is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contributions made thus far.
arXiv Detail & Related papers (2023-08-01T21:43:22Z) - PMC-LLaMA: Towards Building Open-source Language Models for Medicine [62.39105735933138]
Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding.
LLMs struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge.
We describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.
arXiv Detail & Related papers (2023-04-27T18:29:05Z) - Algorithmic Ghost in the Research Shell: Large Language Models and
Academic Knowledge Creation in Management Research [0.0]
The paper looks at the role of large language models in academic knowledge creation.
This includes writing, editing, reviewing, dataset creation and curation.
arXiv Detail & Related papers (2023-03-10T14:25:29Z) - The Semantic Scholar Open Data Platform [79.4493235243312]
Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature.
We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction.
The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings.
arXiv Detail & Related papers (2023-01-24T17:13:08Z) - EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations.
Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
arXiv Detail & Related papers (2022-08-01T18:47:03Z) - Machine learning based disease diagnosis: A comprehensive review [0.0]
This review explains how Machine Learning (ML) and Deep Learning (DL) are being used to help in the early identification of numerous diseases.
The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles.
The review then summarizes the most recent trends and approaches in Machine Learning-based Disease Diagnosis (MLBDD)
arXiv Detail & Related papers (2021-12-31T16:25:23Z) - A bibliometric analysis of research based on the Roy Adaptation Model: a
contribution to Nursing [0.0]
To perform a modern bibliometric analysis of the research based on the Roy Adaptation Model, a founding nursing model proposed by Sor Callista Roy in the1970s.
We used information from the two dominant scientific databases, Web Of Science and SCOPUS.
arXiv Detail & Related papers (2020-03-29T14:02:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.