Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
Journals
Information
Special Issues
Transformative Technologies in Healthcare: Harnessing Machine Learning, Deep Learning and...
share announcement

Transformative Technologies in Healthcare: Harnessing Machine Learning, Deep Learning and Large Language Models in Health Informatics

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2201

Special Issue Editors

Dr. Balu Bhasuran

E-Mail Website
Guest Editor
eHEALTH Lab, College of Communication and Information, Florida State University, Tallahassee, FL 32306-2100, USA
Interests: biomedical informatics; electronic health records; machine learning; natural language processing
Dr. Kalpana Raja

E-Mail Website
Guest Editor
Section for Biomedical Informatics & Data Science, School of Medicine, Yale University, New Haven, CT 06510, USA
Interests: natural language processing; machine learning; deep learning; GPT; bioinformatics

Special Issue Information

Dear Colleagues,

The integration of machine learning and artificial intelligence in clinical and biomedical natural language processing (NLP) is transforming healthcare by enhancing data management and improving care quality. The level of accuracy reported in some of the tasks enables the models to be integrated into a clinical workflow for automation. Techniques such as deep learning and transformer models are crucial for fundamental tasks including concept extraction, normalization, and relationship extraction and further facilitate the creation of accurate knowledge graphs that support clinical decision-making. AI-driven tools effectively disambiguate clinical abbreviations and extract adverse drug events, significantly improving the accuracy and safety of automated diagnoses. In addition, analyzing unstructured medical data to identify social determinants of health supports more personalized care strategies. The advances in natural language inference and medication attribute filling support nuanced information extraction from medical narratives. Overall, the application of sophisticated AI and NLP techniques in healthcare optimizes both data utilization and patient management, heralding a new era of AI-driven medical innovation.

Topics of interest include (but are not limited to) the following:

  • Clinical/biomedical concept extraction and/or normalization;
  • Clinical/biomedical relation extraction;
  • Clinical/biomedical abbreviation disambiguation;
  • Social determinants of health (SDoH);
  • Adverse drug event extraction;
  • Medication attribute filling;
  • Progress note understanding;
  • Retrospective case-control study;
  • Network analysis.

This Special Issue invites original research that explores the intersection of clinical and biomedical natural language processing (NLP). We are particularly interested in contributions that examine the breadth of extraction tasks, utilizing advanced machine learning, deep learning, and large language models for data extraction processes. Submissions should highlight the diversity of data sources leveraged and the various forms of outputs generated. We encourage a range of research methodologies, including quantitative, qualitative, and mixed methods. Additionally, case studies and reports are welcome, provided that they demonstrate significant impact and offer valuable insights at a scale relevant to our readership.

Dr. Balu Bhasuran
Dr. Kalpana Raja
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • clinical natural language processing
  • biomedical natural language processing
  • machine learning
  • artificial intelligence
  • healthcare applied AI
  • automated diagnosis
  • knowledge graphs
  • prospective study
  • causal models
  • prompt tuning
  • large language model
  • text generation
  • transformer model

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 8899 KiB  
Article
Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia
by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan and Anam Naz
Information 2024, 15(12), 787; https://doi.org/10.3390/info15120787 - 8 Dec 2024
Viewed by 958
Abstract
Medical diagnosis plays a critical role in the early detection and treatment of diseases by examining symptoms and supporting findings through advanced laboratory testing. Early and accurate diagnosis is essential for detecting medical problems and then prescribing the most effective treatment strategies, especially [...] Read more.
Medical diagnosis plays a critical role in the early detection and treatment of diseases by examining symptoms and supporting findings through advanced laboratory testing. Early and accurate diagnosis is essential for detecting medical problems and then prescribing the most effective treatment strategies, especially in life-threatening diseases such as leukemia. Leukemia, a blood malignancy, is one of the most prevalent cancer types affecting both adults and children. It is caused by the rapid and uncontrolled growth of abnormal white blood cells in the bone marrow. This accumulation interferes with the production of normal blood cells, leading to a weakened immune deficiency, anemia, and bleeding disorders. Conventional leukemia diagnostic methods are time-consuming, manually intensive, and inefficient. This research study proposes an automatic diagnostics prediction of leukemia by analyzing blood images according to the shape of the blast cells using digital image processing and machine learning. The purpose of blood cell detection is to precisely identify and classify diverse blood cells, detecting anomalies associated with blood cancers like leukemia. This supports early diagnosis and monitoring, which leads to more effective treatments and improved results for cancer patients. To accomplish this task, we use digital image processing techniques and then apply the convolutional neural network (CNN) deep learning algorithm to blood sample images. This research employs a multi-stage methodology, including data preparation, data preprocessing, feature extraction, and then classification. While our model is built on a typical CNN architecture, we make significant advances by using preprocessing techniques and hyperparameter tuning. We have modified its layers combination to include convolutional, pooling, and fully connected layers that are optimized for image characteristics. These layers are fine-tuned for better feature extraction and classification accuracy. This study showed that blood cell detection for diagnosing acute leukemia based on images had 99% accuracy and outperformed other advanced models, including DenseNet121, ResNet-50, Incep-tionv3, MobileNet, and EfficientNet. The comprehensive analysis of the results reveals the highest accuracy of leukemia detection as compared to existing studies in the relevant literature. Full article
(This article belongs to the Special Issue Transformative Technologies in Healthcare: Harnessing Machine Learning, Deep Learning and Large Language Models in Health Informatics)
►▼ Show Figures

Graphical abstract

19 pages, 12083 KiB  
Article
An XAI Approach to Melanoma Diagnosis: Explaining the Output of Convolutional Neural Networks with Feature Injection
by Flavia Grignaffini, Enrico De Santis, Fabrizio Frezza and Antonello Rizzi
Information 2024, 15(12), 783; https://doi.org/10.3390/info15120783 - 5 Dec 2024
Viewed by 695
Abstract
Computer-aided diagnosis (CAD) systems, which combine medical image processing with artificial intelligence (AI) to support experts in diagnosing various diseases, emerged from the need to solve some of the problems associated with medical diagnosis, such as long timelines and operator-related variability. The most [...] Read more.
Computer-aided diagnosis (CAD) systems, which combine medical image processing with artificial intelligence (AI) to support experts in diagnosing various diseases, emerged from the need to solve some of the problems associated with medical diagnosis, such as long timelines and operator-related variability. The most explored medical application is cancer detection, for which several CAD systems have been proposed. Among them, deep neural network (DNN)-based systems for skin cancer diagnosis have demonstrated comparable or superior performance to that of experienced dermatologists. However, the lack of transparency in the decision-making process of such approaches makes them “black boxes” and, therefore, not directly incorporable into clinical practice. Trying to explain and interpret the reasons for DNNs’ decisions can be performed by the emerging explainable AI (XAI) techniques. XAI has been successfully applied to DNNs for skin lesion image classification but never when additional information is incorporated during network training. This field is still unexplored; thus, in this paper, we aim to provide a method to explain, qualitatively and quantitatively, a convolutional neural network model with feature injection for melanoma diagnosis. The gradient-weighted class activation mapping and layer-wise relevance propagation methods were used to generate heat maps, highlighting the image regions and pixels that contributed most to the final prediction. In contrast, the Shapley additive explanations method was used to perform a feature importance analysis on the additional handcrafted information. To successfully integrate DNNs into the clinical and diagnostic workflow, ensuring their maximum reliability and transparency in whatever variant they are used is necessary. Full article
(This article belongs to the Special Issue Transformative Technologies in Healthcare: Harnessing Machine Learning, Deep Learning and Large Language Models in Health Informatics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop