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BioMedInformatics
Special Issues
Feature Papers in Clinical Informatics Section
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Feature Papers in Clinical Informatics Section

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Clinical Informatics".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 20247

Special Issue Editor

Dr. José Machado

E-Mail Website1 Website2
Guest Editor
Computer Science and Technology, ALGORITMI Research Centre, University of Minho, 4710-057 Braga, Portugal
Interests: knowledge engineering; health informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, on featured papers in clinical informatics, focuses on the latest research and advancements in the field of clinical informatics. The selected papers cover a wide range of topics, including electronic health records, health data management, decision support systems, telehealth, and healthcare analytics. These papers provide valuable insights into the integration of technology and healthcare, highlighting the potential for improved patient care, enhanced data analysis, and streamlined clinical workflows. Researchers, practitioners, and policymakers will find these feature papers to be a valuable resource in advancing the field of clinical informatics.

Prof. Dr. José Machado
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. BioMedInformatics is an international peer-reviewed open access quarterly 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 1000 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 informatics
  • electronic health records
  • health data management
  • decision support systems
  • telehealth
  • healthcare analytics
  • technology integration
  • patient care
  • data analysis
  • clinical workflows

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

Published Papers (7 papers)

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Research

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14 pages, 3102 KiB  
Article
Analyzing Patterns of Service Utilization Using Graph Topology to Understand the Dynamic of the Engagement of Patients with Complex Problems with Health Services
by Jonas Bambi, Yudi Santoso, Ken Moselle, Stan Robertson, Abraham Rudnick, Ernie Chang and Alex Kuo
BioMedInformatics 2024, 4(2), 1071-1084; https://doi.org/10.3390/biomedinformatics4020060 - 9 Apr 2024
Cited by 3 | Viewed by 1067
Abstract
Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the [...] Read more.
Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients’ engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients’ movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system. Full article
(This article belongs to the Special Issue Feature Papers in Clinical Informatics Section)
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13 pages, 3812 KiB  
Article
Design and Modelling of an Induction Heating Coil to Investigate the Thermal Response of Magnetic Nanoparticles for Hyperthermia Applications
by Philip Drake, Ali Algaddafi, Thomas Swift and Raed A. Abd-Alhameed
BioMedInformatics 2024, 4(2), 1006-1018; https://doi.org/10.3390/biomedinformatics4020056 - 2 Apr 2024
Cited by 1 | Viewed by 1618
Abstract
Magnetic Field Hyperthermia is a technique where tumours are treated through an increase in local temperature upon exposure to alternating magnetic fields (AMFs) that are mediated by magnetic nano-particles (MNPs). In an AMF, these particles heat-up and kill the cells. The relationship between [...] Read more.
Magnetic Field Hyperthermia is a technique where tumours are treated through an increase in local temperature upon exposure to alternating magnetic fields (AMFs) that are mediated by magnetic nano-particles (MNPs). In an AMF, these particles heat-up and kill the cells. The relationship between an AMF and the heating-rate is complex, leading to confusion when comparing data for different MNP and AMF conditions. This work allows for the thermal-response to be monitored at multiple AMF amplitudes while keeping other parameters constant. An induction-heating coil was designed based on a Zero-Voltage-Zero-Current (ZVZC) resonant circuit. The coil operates at 93 kHz with a variable DC drive-voltage (12–30 V). NEC4 software was used to model the magnetic field distribution, and MNPs were synthesised by the coprecipitation method. The magnetic field was found to be uniform at the centre of the coil and ranged from 1 kAm−1 to 12 kAm−1, depending on the DC drive-voltage. The MNPs were found to have a specific absorption rate (SAR) of 1.37 Wg−1[Fe] and 6.13 Wg−1[Fe] at 93 kHz and 2.1 kAm−1 and 12.6 kAm−1, respectively. The measured SAR value was found to be directly proportional to the product of the frequency and field-strength (SARα f Ho). This leads to the recommendation that, when comparing data from various groups, the SAR value should be normalized following this relationship and not using the more common relationship based on the square of the field intensity (SARα f Ho2). Full article
(This article belongs to the Special Issue Feature Papers in Clinical Informatics Section)
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14 pages, 5432 KiB  
Article
Advancing Early Detection of Breast Cancer: A User-Friendly Convolutional Neural Network Automation System
by Annie Dequit and Fatema Nafa
BioMedInformatics 2024, 4(2), 992-1005; https://doi.org/10.3390/biomedinformatics4020055 - 1 Apr 2024
Cited by 1 | Viewed by 1305
Abstract
Background: Deep learning models have shown potential in improving cancer diagnosis and treatment. This study aimed to develop a convolutional neural network (CNN) model to predict Invasive Ductal Carcinoma (IDC), a common type of breast cancer. Additionally, a user-friendly interface was designed to [...] Read more.
Background: Deep learning models have shown potential in improving cancer diagnosis and treatment. This study aimed to develop a convolutional neural network (CNN) model to predict Invasive Ductal Carcinoma (IDC), a common type of breast cancer. Additionally, a user-friendly interface was designed to facilitate the use of the model by healthcare professionals. Methods: The CNN model was trained and tested using a dataset of high-resolution microscopic images derived from 162 whole-mount slide images of breast cancer specimens. These images were meticulously scanned at 40× magnification using a state-of-the-art digital slide scanner to capture detailed information. Each image was then divided into 277,524 patches of 50 × 50 pixels, resulting in a diverse dataset containing 198,738 IDC-negative and 78,786 IDC-positive patches. Results: The model achieved an accuracy of 98.24% in distinguishing between benign and malignant cases, demonstrating its effectiveness in cancer detection. Conclusions: This study suggests that the developed CNN model has promising potential for clinical applications in breast cancer diagnosis and personalized treatment strategies. Our study further emphasizes the importance of accurate and reliable cancer detection methods for timely diagnosis and treatment. This study establishes a foundation for utilizing deep learning models in future cancer treatment research by demonstrating their effectiveness in analyzing large and complex datasets. This approach opens exciting avenues for further research and potentially improves our understanding of cancer and its treatment. Full article
(This article belongs to the Special Issue Feature Papers in Clinical Informatics Section)
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20 pages, 2334 KiB  
Article
A Methodological Approach to Extracting Patterns of Service Utilization from a Cross-Continuum High Dimensional Healthcare Dataset to Support Care Delivery Optimization for Patients with Complex Problems
by Jonas Bambi, Yudi Santoso, Hanieh Sadri, Ken Moselle, Abraham Rudnick, Stan Robertson, Ernie Chang, Alex Kuo, Joseph Howie, Gracia Yunruo Dong, Kehinde Olobatuyi, Mahdi Hajiabadi and Ashlin Richardson
BioMedInformatics 2024, 4(2), 946-965; https://doi.org/10.3390/biomedinformatics4020053 - 1 Apr 2024
Cited by 4 | Viewed by 1269
Abstract
Background: Optimizing care for patients with complex problems entails the integration of clinically appropriate problem-specific clinical protocols, and the optimization of service-system-encompassing clinical pathways. However, alignment of service system operations with Clinical Practice Guidelines (CPGs) is far more challenging than the time-bounded alignment [...] Read more.
Background: Optimizing care for patients with complex problems entails the integration of clinically appropriate problem-specific clinical protocols, and the optimization of service-system-encompassing clinical pathways. However, alignment of service system operations with Clinical Practice Guidelines (CPGs) is far more challenging than the time-bounded alignment of procedures with protocols. This is due to the challenge of identifying longitudinal patterns of service utilization in the cross-continuum data to assess adherence to the CPGs. Method: This paper proposes a new methodology for identifying patients’ patterns of service utilization (PSUs) within sparse high-dimensional cross-continuum health datasets using graph community detection. Result: The result has shown that by using iterative graph community detections, and graph metrics combined with input from clinical and operational subject matter experts, it is possible to extract meaningful functionally integrated PSUs. Conclusions: This introduces the possibility of influencing the reorganization of some services to provide better care for patients with complex problems. Additionally, this introduces a novel analytical framework relying on patients’ service pathways as a foundation to generate the basic entities required to evaluate conformance of interventions to cohort-specific clinical practice guidelines, which will be further explored in our future research. Full article
(This article belongs to the Special Issue Feature Papers in Clinical Informatics Section)
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12 pages, 594 KiB  
Article
Weighted Rank Difference Ensemble: A New Form of Ensemble Feature Selection Method for Medical Datasets
by Arju Manara Begum, M. Rubaiyat Hossain Mondal, Prajoy Podder and Joarder Kamruzzaman
BioMedInformatics 2024, 4(1), 477-488; https://doi.org/10.3390/biomedinformatics4010027 - 10 Feb 2024
Cited by 1 | Viewed by 1313
Abstract
Background: Feature selection (FS), a crucial preprocessing step in machine learning, greatly reduces the dimension of data and improves model performance. This paper focuses on selecting features for medical data classification. Methods: In this work, a new form of ensemble FS method called [...] Read more.
Background: Feature selection (FS), a crucial preprocessing step in machine learning, greatly reduces the dimension of data and improves model performance. This paper focuses on selecting features for medical data classification. Methods: In this work, a new form of ensemble FS method called weighted rank difference ensemble (WRD-Ensemble) has been put forth. It combines three FS methods to produce a stable and diverse subset of features. The three base FS approaches are Pearson’s correlation coefficient (PCC), reliefF, and gain ratio (GR). These three FS approaches produce three distinct lists of features, and then they order each feature by importance or weight. The final subset of features in this study is chosen using the average weight of each feature and the rank difference of a feature across three ranked lists. Using the average weight and rank difference of each feature, unstable and less significant features are eliminated from the feature space. The WRD-Ensemble method is applied to three medical datasets: chronic kidney disease (CKD), lung cancer, and heart disease. These data samples are classified using logistic regression (LR). Results: The experimental results show that compared to the base FS methods and other ensemble FS methods, the proposed WRD-Ensemble method leads to obtaining the highest accuracy value of 98.97% for CKD, 93.24% for lung cancer, and 83.84% for heart disease. Conclusion: The results indicate that the proposed WRD-Ensemble method can potentially improve the accuracy of disease diagnosis models, contributing to advances in clinical decision-making. Full article
(This article belongs to the Special Issue Feature Papers in Clinical Informatics Section)
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Review

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47 pages, 1335 KiB  
Review
Recent Advances in Large Language Models for Healthcare
by Khalid Nassiri and Moulay A. Akhloufi
BioMedInformatics 2024, 4(2), 1097-1143; https://doi.org/10.3390/biomedinformatics4020062 - 16 Apr 2024
Cited by 7 | Viewed by 8679
Abstract
Recent advances in the field of large language models (LLMs) underline their high potential for applications in a variety of sectors. Their use in healthcare, in particular, holds out promising prospects for improving medical practices. As we highlight in this paper, LLMs have [...] Read more.
Recent advances in the field of large language models (LLMs) underline their high potential for applications in a variety of sectors. Their use in healthcare, in particular, holds out promising prospects for improving medical practices. As we highlight in this paper, LLMs have demonstrated remarkable capabilities in language understanding and generation that could indeed be put to good use in the medical field. We also present the main architectures of these models, such as GPT, Bloom, or LLaMA, composed of billions of parameters. We then examine recent trends in the medical datasets used to train these models. We classify them according to different criteria, such as size, source, or subject (patient records, scientific articles, etc.). We mention that LLMs could help improve patient care, accelerate medical research, and optimize the efficiency of healthcare systems such as assisted diagnosis. We also highlight several technical and ethical issues that need to be resolved before LLMs can be used extensively in the medical field. Consequently, we propose a discussion of the capabilities offered by new generations of linguistic models and their limitations when deployed in a domain such as healthcare. Full article
(This article belongs to the Special Issue Feature Papers in Clinical Informatics Section)
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26 pages, 629 KiB  
Review
Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review
by Stella C. Christopoulou
BioMedInformatics 2024, 4(1), 754-779; https://doi.org/10.3390/biomedinformatics4010042 - 4 Mar 2024
Cited by 3 | Viewed by 4115
Abstract
Background: Over the past few years, clinical studies have utilized machine learning in telehealth and smart care for disease management, self-management, and managing health issues like pulmonary diseases, heart failure, diabetes screening, and intraoperative risks. However, a systematic review of machine learning’s use [...] Read more.
Background: Over the past few years, clinical studies have utilized machine learning in telehealth and smart care for disease management, self-management, and managing health issues like pulmonary diseases, heart failure, diabetes screening, and intraoperative risks. However, a systematic review of machine learning’s use in evidence-based telehealth and smart care is lacking, as evidence-based practice aims to eliminate biases and subjective opinions. Methods: The author conducted a mixed methods review to explore machine learning applications in evidence-based telehealth and smart care. A systematic search of the literature was performed during 16 June 2023–27 June 2023 in Google Scholar, PubMed, and the clinical registry platform ClinicalTrials.gov. The author included articles in the review if they were implemented by evidence-based health informatics and concerned with telehealth and smart care technologies. Results: The author identifies 18 key studies (17 clinical trials) from 175 citations found in internet databases and categorizes them using problem-specific groupings, medical/health domains, machine learning models, algorithms, and techniques. Conclusions: Machine learning combined with the application of evidence-based practices in healthcare can enhance telehealth and smart care strategies by improving quality of personalized care, early detection of health-related problems, patient quality of life, patient-physician communication, resource efficiency and cost-effectiveness. However, this requires interdisciplinary expertise and collaboration among stakeholders, including clinicians, informaticians, and policymakers. Therefore, further research using clinicall studies, systematic reviews, analyses, and meta-analyses is required to fully exploit the potential of machine learning in this area. Full article
(This article belongs to the Special Issue Feature Papers in Clinical Informatics Section)
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