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Applied Sciences
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Machine Learning in Medical Applications (Extended Version)
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Machine Learning in Medical Applications (Extended Version)

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 (30 April 2021) | Viewed by 14863

Special Issue Editors

Prof. Dr. Kao-Shing Hwang

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
Interests: machine learning (artificial intelligence); mobile robots; robot vision; visual servoing; motion control; representation learning; path planning; position control; autonomous aerial vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haobin Shi

E-Mail Website
Guest Editor
School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
Interests: intelligent robots; decision support systems; artificial intelligence; multi-agent systems; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Healthcare is an important industry which offers value-based care to millions of people while, at the same time, being a top revenue earner for many countries. Machine learning (ML) in healthcare, medical diagnosis, and treatment is one such area that is seeing gradual acceptance in the industry. Google recently developed a machine-learning algorithm to identify cancerous tumors in mammograms, and researchers at Stanford University are applying deep learning to detecting skin cancer. Machine learning has already been helpful in a variety of situations in healthcare. ML in healthcare helps to analyze thousands of different datapoints and suggest outcomes, provide timely risk scores, and has many other applications. Therefore, the increasingly growing number of applications of machine learning in healthcare allows us a glimpse into a future where data, analysis, and innovation work hand-in-hand to help countless patients. Soon, it will be quite common to find ML-based applications embedded with real-time patient data available from different healthcare systems in multiple countries, thereby increasing the efficacy of new treatment options that were previously unavailable.

This particular collection aims to bring forward recent advances and present state-of-the-art developments in the theoretical and practical aspects of machine learning in healthcare. Since the emergence of deep-learning techniques and advanced computation technologies, many researchers of different backgrounds have contributed to this area, which has benefited from the heterogeneity and interdisciplinary of finding that are now well established. Much has been achieved; however, many challenges still lie ahead. Thus, this Special Issues serves as an essential and timely update on this topic and should be of interest to potential readers. We anticipate attracting high-quality papers that can fully reflect the progress in processing diagnostic information for healthcare. We specifically target contributions focused on novel learning mechanisms and their applications in medicine. Interdisciplinary contributions to this Special Issue will include but are not be limited to the following areas:

  • Validation, analysis, and learning of data representation for medical imaging diagnosis.
  • Theoretical or methodological developments in machine learning for personalized medicine.
  • The applications of machine learning in radiotherapy, chemotherapy, endoscopic images, laryngoscopic images, MRI, CT imaging, etc.
  • Acute treatments or diagnoses for specific clinical domains.

Prof. Dr. Kao-Shing Hwang
Dr. Haobin Shi
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.

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Published Papers (3 papers)

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Research

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18 pages, 2369 KiB  
Article
LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction
by Pengjun Zhai, Chen Wang and Yu Fang
Appl. Sci. 2021, 11(16), 7237; https://doi.org/10.3390/app11167237 - 5 Aug 2021
Cited by 2 | Viewed by 1676
Abstract
Most existing medical event extraction methods have primarily adopted a simplex model based on either pattern matching or deep learning, which ignores the distribution characteristics of entities and events in the medical corpus. They have not categorized the granularity of event elements, leading [...] Read more.
Most existing medical event extraction methods have primarily adopted a simplex model based on either pattern matching or deep learning, which ignores the distribution characteristics of entities and events in the medical corpus. They have not categorized the granularity of event elements, leading to the poor generalization ability of the model. This paper proposes a diagnosis and treatment event extraction method in the Chinese language, fusing long short-level semantic dependency of the corpus, LSLSD, for solving these problems. LSLSD can effectively capture different levels of semantic information within and between event sentences in the electronic medical record (EMR) corpus. Moreover, the event arguments are divided into short word-level and long sentence-level, with the sequence annotation and pattern matching combined to realize multi-granularity argument recognition, as well as to improve the generalization ability of the model. Finally, this paper constructs a diagnosis and treatment event data set of Chinese EMRs by proposing a semi-automatic corpus labeling method, and an enormous number of experiment results show that LSLSD can improve the F1-value of event extraction task by 7.1% compared with the several strong baselines. Full article
(This article belongs to the Special Issue Machine Learning in Medical Applications (Extended Version))
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17 pages, 14460 KiB  
Article
A Roadmap towards Breast Cancer Therapies Supported by Explainable Artificial Intelligence
by Nicola Amoroso, Domenico Pomarico, Annarita Fanizzi, Vittorio Didonna, Francesco Giotta, Daniele La Forgia, Agnese Latorre, Alfonso Monaco, Ester Pantaleo, Nicole Petruzzellis, Pasquale Tamborra, Alfredo Zito, Vito Lorusso, Roberto Bellotti and Raffaella Massafra
Appl. Sci. 2021, 11(11), 4881; https://doi.org/10.3390/app11114881 - 26 May 2021
Cited by 36 | Viewed by 3622
Abstract
In recent years personalized medicine reached an increasing importance, especially in the design of oncological therapies. In particular, the development of patients’ profiling strategies suggests the possibility of promising rewards. In this work, we present an explainable artificial intelligence (XAI) framework based on [...] Read more.
In recent years personalized medicine reached an increasing importance, especially in the design of oncological therapies. In particular, the development of patients’ profiling strategies suggests the possibility of promising rewards. In this work, we present an explainable artificial intelligence (XAI) framework based on an adaptive dimensional reduction which (i) outlines the most important clinical features for oncological patients’ profiling and (ii), based on these features, determines the profile, i.e., the cluster a patient belongs to. For these purposes, we collected a cohort of 267 breast cancer patients. The adopted dimensional reduction method determines the relevant subspace where distances among patients are used by a hierarchical clustering procedure to identify the corresponding optimal categories. Our results demonstrate how the molecular subtype is the most important feature for clustering. Then, we assessed the robustness of current therapies and guidelines; our findings show a striking correspondence between available patients’ profiles determined in an unsupervised way and either molecular subtypes or therapies chosen according to guidelines, which guarantees the interpretability characterizing explainable approaches to machine learning techniques. Accordingly, our work suggests the possibility to design data-driven therapies to emphasize the differences observed among the patients. Full article
(This article belongs to the Special Issue Machine Learning in Medical Applications (Extended Version))
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Review

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21 pages, 1151 KiB  
Review
COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review
by Amir Rehman, Muhammad Azhar Iqbal, Huanlai Xing and Irfan Ahmed
Appl. Sci. 2021, 11(8), 3414; https://doi.org/10.3390/app11083414 - 10 Apr 2021
Cited by 50 | Viewed by 8478
Abstract
COVID-19 has infected 223 countries and caused 2.8 million deaths worldwide (at the time of writing this article), and the death rate is increasing continuously. Early diagnosis of COVID patients is a critical challenge for medical practitioners, governments, organizations, and countries to overcome [...] Read more.
COVID-19 has infected 223 countries and caused 2.8 million deaths worldwide (at the time of writing this article), and the death rate is increasing continuously. Early diagnosis of COVID patients is a critical challenge for medical practitioners, governments, organizations, and countries to overcome the rapid spread of the deadly virus in any geographical area. In this situation, the previous epidemic evidence on Machine Learning (ML) and Deep Learning (DL) techniques encouraged the researchers to play a significant role in detecting COVID-19. Similarly, the rising scope of ML/DL methodologies in the medical domain also advocates its significant role in COVID-19 detection. This systematic review presents ML and DL techniques practiced in this era to predict, diagnose, classify, and detect the coronavirus. In this study, the data was retrieved from three prevalent full-text archives, i.e., Science Direct, Web of Science, and PubMed, using the search code strategy on 16 March 2021. Using professional assessment, among 961 articles retrieved by an initial query, only 40 articles focusing on ML/DL-based COVID-19 detection schemes were selected. Findings have been presented as a country-wise distribution of publications, article frequency, various data collection, analyzed datasets, sample sizes, and applied ML/DL techniques. Precisely, this study reveals that ML/DL technique accuracy lay between 80% to 100% when detecting COVID-19. The RT-PCR-based model with Support Vector Machine (SVM) exhibited the lowest accuracy (80%), whereas the X-ray-based model achieved the highest accuracy (99.7%) using a deep convolutional neural network. However, current studies have shown that an anal swab test is super accurate to detect the virus. Moreover, this review addresses the limitations of COVID-19 detection along with the detailed discussion of the prevailing challenges and future research directions, which eventually highlight outstanding issues. Full article
(This article belongs to the Special Issue Machine Learning in Medical Applications (Extended Version))
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