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Applied Sciences
Special Issues
Machine Learning and Big Data Processing in Medical Decision Making
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Machine Learning and Big Data Processing in Medical Decision Making

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 3708

Special Issue Editors

Dr. José Ignacio Abreu Salas

E-Mail Website
Guest Editor
University Institute for Computer Research, University of Alicante, 03690 Alicante, Spain
Interests: data science; natural language processing
Special Issues, Collections and Topics in MDPI journals
Dr. Yoan Gutiérrez Vázquez

E-Mail Website
Guest Editor
Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
Interests: knowledge extraction; natural language processing; data science
Dr. Ansel Yoan Rodríguez González

E-Mail Website
Guest Editor
CICESE-UT3, Tepic 63155, Mexico
Interests: data mining; pattern recognition; machine learning; evolutionary computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A new era is beginning in the field of healthcare, where theoretical and technological advances in information science will play a central role. Personalized healthcare and telemedicine, electronic health records, the massive adoption of IoT and wearables, and the development of new and better medical devices are just a few examples of the huge amount of data available nowadays. Leveraging big data and machine learning is set to unleash disruptive innovations for the health sector going forward.

The Special Issue on “Machine Learning and Big Data Processing in Medical Decision Making" welcomes submissions exploring cutting-edge research and advances in this field. Potential topics include, but are not limited to, the following:

  • Big Data (BD) and Data Science (DS) in health;
  • Artificial Intelligence (AI) and machine learning (ML) in health;
  • Semantic data-driven (SDD) solutions for health Informatics;
  • SDD solutions for Internet of Things (IoT) in health;
  • Knowledge discovery (KD), representation (KR), and exploitation (KE) applied in health;
  • AI, ML, DS, and SSD solutions for medical physics.

Dr. José Ignacio Abreu Salas
Dr. Yoan Gutiérrez Vázquez
Dr. Ansel Yoan Rodríguez González
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • big data
  • medical decision making
  • artificial intelligence
  • health informatics
  • data science

Published Papers (3 papers)

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Research

19 pages, 2699 KiB  
Article
Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical Data
by Ling Wang, Minglei Shan, Tie Hua Zhou and Keun Ho Ryu
Appl. Sci. 2023, 13(20), 11151; https://doi.org/10.3390/app132011151 - 10 Oct 2023
Cited by 1 | Viewed by 865
Abstract
The task of accurately identifying medical entities and extracting entity relationships from large-scale medical text data has become a hot topic in recent years, aiming to mine potential rules and knowledge. How to conduct in-depth context analysis from biomedical texts, such as medical [...] Read more.
The task of accurately identifying medical entities and extracting entity relationships from large-scale medical text data has become a hot topic in recent years, aiming to mine potential rules and knowledge. How to conduct in-depth context analysis from biomedical texts, such as medical procedures, diseases, therapeutic drugs, and disease characteristics, and identify valuable knowledge in the medical field is our main research content. Through the process of knowledge mining, a deeper understanding of the complex relationships between various factors in diseases can be gained, which holds significant guiding implications for clinical research. An approach based on context semantic analysis is proposed to realize medical entity recognition and entity relationship extraction. In addition, we build a medical knowledge base related to coronary heart disease and combine the NCBI disease dataset and the medical lexicon dataset extracted from the text as the test data of the experiment. Experimental results show that this model can effectively identify entities in medical text data; the WBC model achieved an F1 score of 89.2% in the experiment, while the CSR model achieved an F1 score of 83.4%, and the result is better than other methods. Full article
(This article belongs to the Special Issue Machine Learning and Big Data Processing in Medical Decision Making)
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23 pages, 5642 KiB  
Article
A Knowledge Graph Framework for Dementia Research Data
by Santiago Timón-Reina, Mariano Rincón, Rafael Martínez-Tomás, Bjørn-Eivind Kirsebom and Tormod Fladby
Appl. Sci. 2023, 13(18), 10497; https://doi.org/10.3390/app131810497 - 20 Sep 2023
Viewed by 1031
Abstract
Dementia disease research encompasses diverse data modalities, including advanced imaging, deep phenotyping, and multi-omics analysis. However, integrating these disparate data sources has historically posed a significant challenge, obstructing the unification and comprehensive analysis of collected information. In recent years, knowledge graphs have emerged [...] Read more.
Dementia disease research encompasses diverse data modalities, including advanced imaging, deep phenotyping, and multi-omics analysis. However, integrating these disparate data sources has historically posed a significant challenge, obstructing the unification and comprehensive analysis of collected information. In recent years, knowledge graphs have emerged as a powerful tool to address such integration issues by enabling the consolidation of heterogeneous data sources into a structured, interconnected network of knowledge. In this context, we introduce DemKG, an open-source framework designed to facilitate the construction of a knowledge graph integrating dementia research data, comprising three core components: a KG-builder that integrates diverse domain ontologies and data annotations, an extensions ontology providing necessary terms tailored for dementia research, and a versatile transformation module for incorporating study data. In contrast with other current solutions, our framework provides a stable foundation by leveraging established ontologies and community standards and simplifies study data integration while delivering solid ontology design patterns, broadening its usability. Furthermore, the modular approach of its components enhances flexibility and scalability. We showcase how DemKG might aid and improve multi-modal data investigations through a series of proof-of-concept scenarios focused on relevant Alzheimer’s disease biomarkers. Full article
(This article belongs to the Special Issue Machine Learning and Big Data Processing in Medical Decision Making)
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15 pages, 2839 KiB  
Article
Wart-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model
by Shuangbao Song, Botao Zhang, Xingqian Chen, Qiang Xu and Jia Qu
Appl. Sci. 2023, 13(11), 6542; https://doi.org/10.3390/app13116542 - 27 May 2023
Cited by 1 | Viewed by 954
Abstract
Warts are a prevalent condition worldwide, affecting approximately 10% of the global population. In this study, a machine learning method based on a dendritic neuron model is proposed for wart-treatment efficacy prediction. To prevent premature convergence and improve the interpretability of the model [...] Read more.
Warts are a prevalent condition worldwide, affecting approximately 10% of the global population. In this study, a machine learning method based on a dendritic neuron model is proposed for wart-treatment efficacy prediction. To prevent premature convergence and improve the interpretability of the model training process, an effective heuristic algorithm, i.e., the covariance matrix adaptation evolution strategy (CMA-ES), is incorporated as the training method of the dendritic neuron model. Two common datasets of wart-treatment efficacy, i.e., the cryotherapy dataset and the immunotherapy dataset, are used to verify the effectiveness of the proposed method. The proposed CMA-ES-based dendritic neuron model achieves promising results, with average classification accuracies of 0.9012 and 0.8654 on the two datasets, respectively. The experimental results indicate that the proposed method achieves better or more competitive prediction results than six common machine learning models. In addition, the trained dendritic neuron model can be simplified using a dendritic pruning mechanism. Finally, an effective wart-treatment efficacy prediction method based on a dendritic neuron model, which can provide decision support for physicians, is proposed in this paper. Full article
(This article belongs to the Special Issue Machine Learning and Big Data Processing in Medical Decision Making)
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