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Diagnostics
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Advances in Disease Prediction
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Advances in Disease Prediction

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5283

Special Issue Editors

Prof. Dr. Chi-Chang Chang

E-Mail Website
Guest Editor
Chair of Medical Informatics Department, Chung Shan Medical University, Taichung City, Taiwan
Interests: medical informatics; clinical decision analysis; simulation modeling; shared medical decision making
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Osman Hayran

E-Mail Website
Guest Editor
School of Medicine, Istanbul Medipol University, Kavacık Mah. Ekinciler Cad. No.19, Kavacık Kavşağı, Beykoz, 34810 Istanbul, Turkey
Interests: epidemiology; health care management; social medicine; public health; healthcare policies; social determinants of health
Dr. Chalong Cheewakriangkrai

E-Mail Website
Guest Editor
Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: clinical epidemiology; chemotherapy and targeted therapy; surgery; gynecologic Oncology

Special Issue Information

Dear Colleagues,

In the era of digital health, complex and ubiquitous health data are collected from various sources, such as electronic health records, medical monitoring devices, wearable health systems, and mobile phone applications. Big data analytics techniques such as statistical analysis, machine learning, deep learning, generative intelligence, and digital twins can be applied to build innovative advances in disease prediction. Recently, the need for automated/intelligent laboratory recommendation systems has increased to provide more accurate and faster diagnoses. The fusion of various clinical data sources promoted by advances in laboratory approaches can significantly improve the diagnosis of diseases, illustrating how the digital revolution transforms clinical diagnostic practice. Based on concrete evidence, it is crucial and has a significant impact on the implementation of health care and programs. This fact highlights the important role of early diagnostics, including disease control, a range of treatment options, improved health services, improved health disparities and quality of life, etc. The aim of this Special Issue is to provide a comprehensive and current collection of state-of-the-art studies to advance disease prediction. Practical experience and experiments on the above-mentioned innovative analysis issues are also welcomed.

Prof. Dr. Chi-Chang Chang
Prof. Dr. Osman Hayran
Dr. Chalong Cheewakriangkrai
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. Diagnostics 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 2600 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

  • early diagnostics
  • medical big data analytics
  • intelligent diagnostic models in public health
  • survival analysis and health hazard evaluations
  • machine learning and generative intelligence
  • intelligent digital twins
  • automated/intelligent laboratory recommendation systems

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

Published Papers (5 papers)

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Research

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24 pages, 2131 KiB  
Article
The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation
by Luiza Camelia Nechita, Ancuta Elena Tupu, Aurel Nechita, Daniel Voipan, Andreea Elena Voipan, Dana Tutunaru and Carmina Liana Musat
Diagnostics 2025, 15(7), 856; https://doi.org/10.3390/diagnostics15070856 - 27 Mar 2025
Viewed by 168
Abstract
Background/Objectives: Cardiac arrhythmias impact quality of life (QoL) and are often linked to psychological distress. This study examines the relationship between QoL, depression, and arrhythmias using AI-assisted analysis to enhance patient management. Methods: A total of 145 patients with arrhythmias were [...] Read more.
Background/Objectives: Cardiac arrhythmias impact quality of life (QoL) and are often linked to psychological distress. This study examines the relationship between QoL, depression, and arrhythmias using AI-assisted analysis to enhance patient management. Methods: A total of 145 patients with arrhythmias were assessed using an SF-36 health survey (QoL) and a PHQ-9 questionnaire (depression). Statistical analyses included regression, clustering, and AI-based models such as K-means and logistic regression to identify risk factors and patient subgroups. Results: Patients with comorbidities had lower QoL and higher depression scores. PHQ-9 scores negatively correlated with SF-36 mental health components. AI-assisted clustering identified distinct patient subgroups, with older individuals and those with longer disease duration exhibiting the lowest QoL. Logistic regression predicted depression with 93% accuracy, and XGBoost achieved an AUC of 0.97. Conclusions: QoL plays a key role in arrhythmia management, with depression significantly influencing outcomes. AI-driven predictive models offer personalized interventions, improving early detection and treatment. Future research should integrate wearable technology and AI-based monitoring to optimize patient care. Full article
(This article belongs to the Special Issue Advances in Disease Prediction)
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15 pages, 4561 KiB  
Article
Data from Emergency Medical Service Activities: A Novel Approach to Monitoring COVID-19 and Other Infectious Diseases
by Daniele del Re, Luigi Palla, Paolo Meridiani, Livia Soffi, Michele Tancredi Loiudice, Martina Antinozzi and Maria Sofia Cattaruzza
Diagnostics 2025, 15(2), 181; https://doi.org/10.3390/diagnostics15020181 - 14 Jan 2025
Cited by 1 | Viewed by 632 | Correction
Abstract
Background: Italy, particularly the northern region of Lombardy, has experienced very high rates of COVID-19 cases and deaths. Several indicators, i.e., the number of new positive cases, deaths and hospitalizations, have been used to monitor virus spread, but all suffer from biases. [...] Read more.
Background: Italy, particularly the northern region of Lombardy, has experienced very high rates of COVID-19 cases and deaths. Several indicators, i.e., the number of new positive cases, deaths and hospitalizations, have been used to monitor virus spread, but all suffer from biases. The aim of this study was to evaluate an alternative data source from Emergency Medical Service (EMS) activities for COVID-19 monitoring. Methods: Calls to the emergency number (112) in Lombardy (years 2015–2022) were studied and their overlap with the COVID-19 pandemic, influenza and official mortality peaks were evaluated. Modeling it as a counting process, a specific cause contribution (i.e., COVID-19 symptoms, the “signal”) was identified and enucleated from all other contributions (the “background”), and the latter was subtracted from the total observed number of calls using statistical methods for excess event estimation. Results: A total of 6,094,502 records were analyzed and filtered for respiratory and cardiological symptoms to identify potential COVID-19 patients, yielding 742,852 relevant records. Results show that EMS data mirrored the time series of cases or deaths in Lombardy, with good agreement also being found with seasonal flu outbreaks. Conclusions: This novel approach, combined with a machine learning predictive approach, could be a powerful public health tool to signal the start of disease outbreaks and monitor the spread of infectious diseases. Full article
(This article belongs to the Special Issue Advances in Disease Prediction)
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Review

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24 pages, 1109 KiB  
Review
Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review
by Sarfuddin Azmi, Faisal Kunnathodi, Haifa F. Alotaibi, Waleed Alhazzani, Mohammad Mustafa, Ishtiaque Ahmad, Riyasdeen Anvarbatcha, Miltiades D. Lytras and Amr A. Arafat
Diagnostics 2025, 15(3), 396; https://doi.org/10.3390/diagnostics15030396 - 6 Feb 2025
Viewed by 1234
Abstract
Purpose: This review aims to explore the clinical and research applications of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in understanding, predicting, and managing obesity. It assesses the use of AI tools to identify obesity-related risk factors, predict outcomes, [...] Read more.
Purpose: This review aims to explore the clinical and research applications of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in understanding, predicting, and managing obesity. It assesses the use of AI tools to identify obesity-related risk factors, predict outcomes, personalize treatments, and improve healthcare interventions for obesity. Methods: A comprehensive literature search was conducted using PubMed and Google Scholar, with keywords including “artificial intelligence”, “machine learning”, “deep learning”, “obesity”, “obesity management”, and related terms. Studies focusing on AI’s role in obesity research, management, and therapeutic interventions were reviewed, including observational studies, systematic reviews, and clinical applications. Results: This review identifies numerous AI-driven models, such as ML and DL, used in obesity prediction, patient stratification, and personalized management strategies. Applications of AI in obesity research include risk prediction, early detection, and individualization of treatment plans. AI has facilitated the development of predictive models utilizing various data sources, such as genetic, epigenetic, and clinical data. However, AI models vary in effectiveness, influenced by dataset type, research goals, and model interpretability. Performance metrics such as accuracy, precision, recall, and F1-score were evaluated to optimize model selection. Conclusions: AI offers promising advancements in obesity management, enabling more personalized and efficient care. While technology presents considerable potential, challenges such as data quality, ethical considerations, and technical requirements remain. Addressing these will be essential to fully harness AI’s potential in obesity research and treatment, supporting a shift toward precision healthcare. Full article
(This article belongs to the Special Issue Advances in Disease Prediction)
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Other

Jump to: Research, Review

2 pages, 680 KiB  
Correction
Correction: del Re et al. Data from Emergency Medical Service Activities: A Novel Approach to Monitoring COVID-19 and Other Infectious Diseases. Diagnostics 2025, 15, 181
by Daniele del Re, Luigi Palla, Paolo Meridiani, Livia Soffi, Michele Tancredi Loiudice, Martina Antinozzi and Maria Sofia Cattaruzza
Diagnostics 2025, 15(6), 745; https://doi.org/10.3390/diagnostics15060745 - 17 Mar 2025
Viewed by 166
Abstract
In the original publication [...] Full article
(This article belongs to the Special Issue Advances in Disease Prediction)
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16 pages, 1127 KiB  
Systematic Review
Prediction Models for Perioperative Blood Transfusion in Patients Undergoing Gynecologic Surgery: A Systematic Review
by Zhongmian Pan and Kittipat Charoenkwan
Diagnostics 2024, 14(18), 2018; https://doi.org/10.3390/diagnostics14182018 - 12 Sep 2024
Viewed by 1138
Abstract
This systematic review aimed to evaluate prediction models for perioperative blood transfusion in patients undergoing gynecologic surgery. Given the inherent risks associated with blood transfusion and the critical need for accurate prediction, this study identified and assessed models based on their development, validation, [...] Read more.
This systematic review aimed to evaluate prediction models for perioperative blood transfusion in patients undergoing gynecologic surgery. Given the inherent risks associated with blood transfusion and the critical need for accurate prediction, this study identified and assessed models based on their development, validation, and predictive performance. The review included five studies encompassing various surgical procedures and approaches. Predicting factors commonly used across these models included preoperative hematocrit, race, surgical route, and uterine fibroid characteristics. However, the review highlighted significant variability in the definition of perioperative periods, a lack of standardization in transfusion criteria, and a high risk of bias in most models due to methodological issues, such as a low number of events per variable, inappropriate handling of continuous and categorical predictors, inappropriate handling of missing data, improper methods of predictor selection, inappropriate measurement methods for model performance, and inadequate evaluations of model overfitting and optimism in model performance. Despite some models demonstrating good discrimination and calibration, the overall quality and external validation of these models were limited. Consequently, there is a clear need for more robust and externally validated models to improve clinical decision-making and patient outcomes in gynecologic surgery. Future research should focus on refining these models, incorporating rigorous validation, and adhering to standardized reporting practices. Full article
(This article belongs to the Special Issue Advances in Disease Prediction)
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