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Applied Sciences
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Advanced Machine Learning in Medical Informatics
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Advanced Machine Learning in Medical Informatics

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 (31 May 2022) | Viewed by 6420

Special Issue Editor

Prof. Dr. Vili Podgorelec

E-Mail Website
Guest Editor
Intelligent Systems Laboratory, University of Maribor, 2000 Maribor, Slovenia
Interests: data analytics; data science; machine learning; knowledge discovery; software engineering; medical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning has quickly found its place in medical informatics, where the goal is to develop methods and technologies for the acquisition, processing, and analysis of patient data. With the development of information technology, the amount of data has increased tremendously, the types of data and methods of obtaining them are becoming very diverse, while the integration and fusion of multimodal data from heterogeneous sources is increasingly required. If we also consider the tendency towards increasing the personalization of healthcare procedures and patient-tailored medical decisions, we can quickly realize that medical informatics presents exceptional challenges to the development of advanced machine learning (ML) and artificial intelligence (AI) methods and algorithms, while the advancements in ML offer solutions with great potential for solving medical informatics problems.

This Special Issue will address the most recent advances in machine learning approaches and techniques in medical informatics. Thus, contributions are expected which present original research on machine learning with real-world applications in medicine and healthcare.

Topics for this Special Issue include, but are not limited to, the following:

  • Big data analytics;
  • Knowledge discovery and data mining;
  • Explainable and transparent ML models;
  • Advancements in classical ML methods;
  • Deep learning, transfer learning and deep mining;
  • Optimization of ML methods and algorithms;
  • Hybrid ML methods and ensemble learning;
  • Biomedical knowledge acquisition and management;
  • Temporal and spatial representation and reasoning;
  • Biomedical imaging and signal processing;
  • Personalized and patient-tailored medicine;
  • Mining healthcare processes;
  • ML solutions for telemedicine, e-health and ambient assisted living;
  • Applications of ML in various fields of medicine and healthcare.

Prof. Dr. Vili Podgorelec
Guest Editor

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
  • artificial intelligence
  • data science
  • knowledge discovery
  • classification
  • prediction
  • anomaly detection
  • medicine
  • healthcare
  • medical informatics

Published Papers (3 papers)

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Research

14 pages, 1404 KiB  
Article
Specific Relation Attention-Guided Graph Neural Networks for Joint Entity and Relation Extraction in Chinese EMR
by Yali Pang, Xiaohui Qin and Zhichang Zhang
Appl. Sci. 2022, 12(17), 8493; https://doi.org/10.3390/app12178493 - 25 Aug 2022
Cited by 2 | Viewed by 1422
Abstract
Electronic medical records (EMRs) contain a variety of valuable medical entities and their relations. The extraction of medical entities and their relations has important application value in the structuring of EMR and the development of various types of intelligent assistant medical systems, and [...] Read more.
Electronic medical records (EMRs) contain a variety of valuable medical entities and their relations. The extraction of medical entities and their relations has important application value in the structuring of EMR and the development of various types of intelligent assistant medical systems, and hence is a hot issue in intelligent medicine research. In recent years, most research aims to firstly identify entities and then to recognize the relations between the entities, and often suffers from many redundant operations. Furthermore, the challenge remains of identifying overlapping relation triplets along with the entire medical entity boundary and detecting multi-type relations. In this work, we propose a Specific Relation Attention-guided Graph Neural Networks (SRAGNNs) model to jointly extract entities and their relations in Chinese EMR, which uses sentence information and attention-guided graph neural networks to perceive the features of every relation in a sentence and then to extract those relations. In addition, a specific sentence representation is constructed for every relation, and sequence labeling is performed to extract its corresponding head and tail entities. Experiments on a medical evaluation dataset and a manually labeled Chinese EMR dataset show that our model improves the performance of Chinese medical entities and relation extraction. Full article
(This article belongs to the Special Issue Advanced Machine Learning in Medical Informatics)
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12 pages, 1697 KiB  
Article
Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer
by Jae-Kwon Kim, Sun-Jung Lee, Sung-Hoo Hong and In-Young Choi
Appl. Sci. 2022, 12(16), 8156; https://doi.org/10.3390/app12168156 - 15 Aug 2022
Cited by 7 | Viewed by 1975
Abstract
Clinical decision support systems (CDSSs) enable users to make decisions based on clinical data from electronic medical records, facilitating personalized precision medicine treatments. A digital twin (DT) approach enables the interoperability between physical and virtual environments through data analysis using machine learning (ML). [...] Read more.
Clinical decision support systems (CDSSs) enable users to make decisions based on clinical data from electronic medical records, facilitating personalized precision medicine treatments. A digital twin (DT) approach enables the interoperability between physical and virtual environments through data analysis using machine learning (ML). By combining DT with the prostate cancer (PCa) process, it is possible to predict cancer prognosis. In this study, we propose a DT-based prediction model for clinical decision-making in the PCa process. Pathology and biochemical recurrence (BCR) were predicted with ML using data from a clinical data warehouse and the PCa process. The DT model was developed using data from 404 patients. The BCR prediction accuracy increased according to the amount of data used, and reached as high as 96.25% when all data were used. The proposed DT-based predictive model can help provide a clinical decision support system for PCa. Further, it can be used to improve medical processes, promote health, and reduce medical costs and problems. Full article
(This article belongs to the Special Issue Advanced Machine Learning in Medical Informatics)
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14 pages, 3084 KiB  
Article
Posture Monitoring for Health Care of Bedridden Elderly Patients Using 3D Human Skeleton Analysis via Machine Learning Approach
by Jui-Chiu Chiang, Wen-Nung Lie, Hsiu-Chen Huang, Kuan-Ting Chen, Jhih-Yuan Liang, Yu-Chia Lo and Wei-Hao Huang
Appl. Sci. 2022, 12(6), 3087; https://doi.org/10.3390/app12063087 - 17 Mar 2022
Cited by 5 | Viewed by 2607
Abstract
For bedridden elderly people, pressure ulcer is the most common and serious complication and could be prevented by regular repositioning. However, due to a shortage of long-term care workers, repositioning might not be implemented as often as required. Posture monitoring by using modern [...] Read more.
For bedridden elderly people, pressure ulcer is the most common and serious complication and could be prevented by regular repositioning. However, due to a shortage of long-term care workers, repositioning might not be implemented as often as required. Posture monitoring by using modern health/medical caring technology can potentially solve this problem. We propose a RGB-D camera system to recognize the posture of the bedridden elderly patients based on the analysis of 3D human skeleton which consists of articulated joints. Since practically most bedridden patients were covered with a blanket, only four 3D joints were used in our system. After the recognition of the posture, a warning message will be sent to the caregiver for assistance if the patient stays in the same posture for more than a predetermined period (e.g., two hours). Experimental results indicate that our proposed method is capable of achieving a high accuracy in posture recognition (above 95%). To the best of our knowledge, this application of using human skeleton analysis for patient care is novel. The proposed scheme is promising for clinical applications and will undertake an intensive test in health care facilities in the near future after redesigning a proper RGB-D (Red-Green-Blue-Depth) camera system. In addition, a desktop computer can be used for multi-point monitoring to reduce cost, since real-time processing is not required in this application. Full article
(This article belongs to the Special Issue Advanced Machine Learning in Medical Informatics)
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