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Application of Decision Support Systems in Biomedical Engineering

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 10056

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Special Issue Editor

Dr. Maria Rizzi

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Guest Editor

Department of Electrics and Information Engineering, Politecnico di Bari, 70125 Bari, Italy
Interests: computer aided detection and diagnosis systems for biomedical signals; monitoring systems for health-care; analysis and synthesis of digital electronic systems
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Special Issue Information

Dear Colleagues,

In the framework of healthcare facilities, the decision-making phase is frequent and of particular relevance to achieve results responding to expectations. The fusion of technology and medical science allowed for the development of decision support systems (DSSs) that are able to make good decisions in extremely complex and difficult medical diagnosis cases. Two different application contexts of DSSs can be configured, including the expert knowledge-provider context and the end user context. For example, a DSS could assist clinicians in solving diagnostic and therapeutic problems by making use of patient-related parameters as inputs which have to be combined with models and algorithms. The choice/adoption of a particular DSS depends on several criteria such as the complexity of the problem, the simplicity of the approach, the typology of data available, the expertise of the decision makers, and so on. A suitable and appropriate DSS has to be user-friendly and accurate.

This Special Issue addresses the most recent applications of decision support systems in biomedical engineering in the expert knowledge provider context and the end user context. Original high-quality papers and review articles are welcome and incentivized. In particular, topics of interest for this Special Issue include, but are not limited to, the following:

Decision support systems for biomedical systems;
Methods for the classification of biosignals;
Biosignal/bioimage feature extraction;
Algorithms to improve the quality of biosignals;
Artificial Intelligence tools for biosignal/bioimage analysis
Clinical decision support system;
Medical healthcare systems.

Dr. Maria Rizzi
Guest Editor

Manuscript Submission Information

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Keywords

advanced healthcare methods
biomedical image analysis
computer-aided detection systems
computer-aided diagnosis systems
monitoring systems

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Published Papers (5 papers)

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Research

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16 pages, 1837 KiB

Open AccessArticle

A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields

by Sivan Albagli-Kim and Dizza Beimel

Appl. Sci. 2025, 15(3), 1561; https://doi.org/10.3390/app15031561 - 4 Feb 2025

Viewed by 753

Abstract

Healthcare 4.0 addresses modernization and digital transformation challenges, such as home-based care and precision treatments, by leveraging advanced technologies to enhance accessibility and efficiency. Semantic technologies, particularly knowledge graphs (KGs), have proven instrumental in representing interconnected medical data and improving clinical decision-support systems. We previously introduced a semantic framework to assist medical experts during patient interactions. Operating iteratively, the framework prompts medical experts with relevant questions based on patient input, progressing toward accurate diagnoses in time-constrained settings. It comprises two components: (a) a KG representing symptoms, diseases, and their relationships, and (b) algorithms that generate questions and prioritize hypotheses—a ranked list of symptom–disease pairs. An earlier extension enriched the KG with a symptom ontology, incorporating hierarchical structures and inheritance relationships to improve accuracy and question-generation capabilities. This paper further extends the framework by introducing strategies tailored to specific medical domains. Strategies integrate domain-specific knowledge and algorithms, refining decision making while maintaining the iterative nature of expert–patient interactions. We demonstrate this approach using an emergency medicine case study, focusing on life-threatening conditions. The KG is enriched with attributes tailored to emergency contexts and supported by dedicated algorithms. Boolean rules attached to graph edges evaluate to TRUE or FALSE at runtime based on patient-specific data. These enhancements optimize decision making by embedding domain-specific goal-oriented knowledge and inference processes, providing a scalable and adaptable solution for diverse medical contexts. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Biomedical Engineering)

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13 pages, 2867 KiB

Open AccessArticle

Innovative Approaches to Clinical Diagnosis: Transfer Learning in Facial Image Classification for Celiac Disease Identification

by Elif Keskin Bilgiç, İnci Zaim Gökbay and Yusuf Kayar

Appl. Sci. 2024, 14(14), 6207; https://doi.org/10.3390/app14146207 - 17 Jul 2024

Cited by 3 | Viewed by 2051

Abstract

Background: Celiac disease arises from gluten consumption and shares symptoms with other conditions, leading to delayed diagnoses. Untreated celiac disease heightens the risk of autoimmune disorders, neurological issues, and certain cancers like lymphoma while also impacting skin health due to intestinal disruptions. This study uses facial photos to distinguish individuals with celiac disease from those without. Surprisingly, there is a lack of research involving transfer learning for this purpose despite its benefits such as faster training, enhanced performance, and reduced overfitting. While numerous studies exist on endoscopic intestinal photo classification and a few have explored the link between facial morphology measurements and celiac disease, none have concentrated on diagnosing celiac disease through facial photo classification. Methods: This study sought to apply transfer learning techniques with VGG16 to address a gap in research by identifying distinct facial features that differentiate patients with celiac disease from healthy individuals. A dataset containing a total of 200 facial images of adult individuals with and without celiac condition was utilized. Half of the dataset had a ratio of 70% females to 30% males with celiac condition, and the rest had a ratio of 60% females to 40% males without celiac condition. Among those with celiac condition, 28 were newly diagnosed and 72 had been previously diagnosed, with 25 not adhering to a gluten-free diet and 47 partially adhering to such a diet. Results: Utilizing transfer learning, the model achieved a 73% accuracy in classifying the facial images of the patients during testing, with corresponding precision, recall, and F1 score values of 0.54, 0.56, and 0.52, respectively. The training process involved 50,178 parameters, showcasing the model’s efficacy in diagnostic image analysis. Conclusions: The model correctly classified approximately three-quarters of the test images. While this is a reasonable level of accuracy, it also suggests that there is room for improvement as the dataset contains images that are inherently difficult to classify even for humans. Increasing the proportion of newly diagnosed patients in the dataset and expanding the dataset size could notably improve the model’s efficacy. Despite being the first study in this field, further refinement holds promise for the development of a diagnostic tool for celiac disease using transfer learning in medical image analysis, addressing the lack of prior studies in this area. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Biomedical Engineering)

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21 pages, 9113 KiB

Open AccessArticle

Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare

by Francesco Berloco, Pietro Maria Marvulli, Vladimiro Suglia, Simona Colucci, Gaetano Pagano, Lucia Palazzo, Maria Aliani, Giorgio Castellana, Patrizia Guido, Giovanni D’Addio and Vitoantonio Bevilacqua

Appl. Sci. 2024, 14(14), 6084; https://doi.org/10.3390/app14146084 - 12 Jul 2024

Cited by 2 | Viewed by 1785

Abstract

Artificial intelligence algorithms have become extensively utilized in survival analysis for high-dimensional, multi-source data. However, due to their complexity, these methods often yield poorly interpretable outcomes, posing challenges in the analysis of several conditions. One of these conditions is obstructive sleep apnea, a sleep disorder characterized by the simultaneous occurrence of comorbidities. Survival analysis provides a potential solution for assessing and categorizing the severity of obstructive sleep apnea, aiding personalized treatment strategies. Given the critical role of time in such scenarios and considering limitations in model interpretability, time-dependent explainable artificial intelligence algorithms have been developed in recent years for direct application to basic Machine Learning models, such as Cox regression and survival random forest. Our work aims to enhance model selection in OSA survival analysis using time-dependent XAI for Machine Learning and Deep Learning models. We developed an end-to-end pipeline, training several survival models and selecting the best performers. Our top models—Cox regression, Cox time, and logistic hazard—achieved good performance, with C-index scores of 0.81, 0.78, and 0.77, and Brier scores of 0.10, 0.12, and 0.11 on the test set. We applied SurvSHAP methods to Cox regression and logistic hazard to investigate their behavior. Although the models showed similar performance, our analysis established that the results of the log hazard model were more reliable and useful in clinical practice compared to those of Cox regression in OSA scenarios. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Biomedical Engineering)

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Review

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20 pages, 430 KiB

Open AccessReview

Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review

by Guilherme Almeida, Fernanda Brito Correia, Ana Rosa Borges and Jorge Bernardino

Appl. Sci. 2024, 14(22), 10523; https://doi.org/10.3390/app142210523 - 15 Nov 2024

Cited by 1 | Viewed by 2671

Abstract

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Biomedical Engineering)

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29 pages, 705 KiB

Open AccessReview

AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review

by Mercy Edoho, Catherine Mooney and Lan Wei

Appl. Sci. 2024, 14(16), 7398; https://doi.org/10.3390/app14167398 - 22 Aug 2024

Viewed by 2279

Abstract

About 70 million people globally have been diagnosed with epilepsy. Electroencephalogram (EEG) devices are the primary method for identifying and monitoring seizures. The use of EEG expands the preclinical research involving the long-term recording of neuro-activities in rodent models of epilepsy targeted towards the efficient testing of prospective antiseizure medications. Typically, trained epileptologists visually analyse long-term EEG recordings, which is time-consuming and subject to expert variability. Automated epileptiform discharge detection using machine learning or deep learning methods is an effective approach to tackling these challenges. This systematic review examined and summarised the last 30 years of research on detecting epileptiform discharge in rodent models of epilepsy using machine learning and deep learning methods. A comprehensive literature search was conducted on two databases, PubMed and Google Scholar. Following the PRISMA protocol, the 3021 retrieved articles were filtered to 21 based on inclusion and exclusion criteria. An additional article was obtained through the reference list. Hence, 22 articles were selected for critical analysis in this review. These articles revealed the seizure type, features and feature engineering, machine learning and deep learning methods, training methodologies, evaluation metrics so far explored, and models deployed for real-world validation. Although these studies have advanced the field of epilepsy research, the majority of the models are experimental. Further studies are required to fill in the identified gaps and expedite preclinical research in epilepsy, ultimately leading to translational research. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Biomedical Engineering)

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