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Diagnostics
Special Issues
Information-Driven Computer-Aided Diagnosis and Decision Support System
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Information-Driven Computer-Aided Diagnosis and Decision Support System

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: 31 August 2025 | Viewed by 4534

Special Issue Editors

Prof. Dr. Wen-Shan Jian

E-Mail Website
Guest Editor
School of Health Care Administration, Taipei Medical University, Taipei 11031, Taiwan
Interests: Internet of Things; healthcare management; artificial intelligence; data visualization
Special Issues, Collections and Topics in MDPI journals
Dr. Shuo-Chen Chien

E-Mail Website
Guest Editor
Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 11031, Taiwan
Interests: artificial intelligence; data visualization; natural language processing; CDSS alert system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emergence and rapid advancement of information-driven methodologies have significantly transformed the field of medical diagnostics and decision-making. The integration of computer-aided diagnosis and decision support systems in recent years has led to notable improvements in diagnostic accuracy, treatment planning, and patient outcomes. These advancements harness the capabilities of machine learning, artificial intelligence, and data analytics to provide enhanced insights and support to healthcare professionals.

We invite researchers to submit original research articles that delve into the development, implementation, and impact of these advanced systems. Topics of interest include, but are not limited to, machine learning algorithms in healthcare, AI-driven diagnostic tools, predictive modeling, clinical decision support systems, and the integration of healthcare informatics in clinical settings. By contributing to this Special Issue, you will help advance our understanding and the application of cutting-edge technologies in healthcare, ultimately leading to better patient care and clinical efficiency.

Prof. Dr. Wen-Shan Jian
Dr. Shuo-Chen Chien
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

  • computer-aided diagnosis
  • decision support systems
  • machine learning in healthcare
  • artificial intelligence in medicine
  • medical data analytics
  • predictive modeling
  • clinical decision-making
  • healthcare informatics
  • diagnostic accuracy
  • patient outcomes

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

Published Papers (2 papers)

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Research

27 pages, 4989 KiB  
Article
A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial Infarction
by Matthew Hodgman, Cristian Minoccheri, Michael Mathis, Emily Wittrup and Kayvan Najarian
Diagnostics 2024, 14(16), 1741; https://doi.org/10.3390/diagnostics14161741 - 10 Aug 2024
Viewed by 1431
Abstract
Background: Acute myocardial infarctions are deadly to patients and burdensome to healthcare systems. Most recorded infarctions are patients’ first, occur out of the hospital, and often are not accompanied by cardiac comorbidities. The clinical manifestations of the underlying pathophysiology leading to an infarction [...] Read more.
Background: Acute myocardial infarctions are deadly to patients and burdensome to healthcare systems. Most recorded infarctions are patients’ first, occur out of the hospital, and often are not accompanied by cardiac comorbidities. The clinical manifestations of the underlying pathophysiology leading to an infarction are not fully understood and little effort exists to use explainable machine learning to learn predictive clinical phenotypes before hospitalization is needed. Methods: We extracted outpatient electronic health record data for 2641 case and 5287 matched-control patients, all without pre-existing cardiac diagnoses, from the Michigan Medicine Health System. We compare six different interpretable, feature extraction approaches, including temporal computational phenotyping, and train seven interpretable machine learning models to predict the onset of first acute myocardial infarction within six months. Results: Using temporal computational phenotypes significantly improved the model performance compared to alternative approaches. The mean cross-validation test set performance exhibited area under the receiver operating characteristic curve values as high as 0.674. The most consistently predictive phenotypes of a future infarction include back pain, cardiometabolic syndrome, family history of cardiovascular diseases, and high blood pressure. Conclusions: Computational phenotyping of longitudinal health records can improve classifier performance and identify predictive clinical concepts. State-of-the-art interpretable machine learning approaches can augment acute myocardial infarction risk assessment and prioritize potential risk factors for further investigation and validation. Full article
(This article belongs to the Special Issue Information-Driven Computer-Aided Diagnosis and Decision Support System)
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20 pages, 1275 KiB  
Article
Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A Feasibility Study
by Haroon Miah, Dimitrios Kollias, Giacinto Luca Pedone, Drew Provan and Frederick Chen
Diagnostics 2024, 14(13), 1352; https://doi.org/10.3390/diagnostics14131352 - 26 Jun 2024
Cited by 1 | Viewed by 2231
Abstract
Primary Immune Thrombocytopenia (ITP) is a rare autoimmune disease characterised by the immune-mediated destruction of peripheral blood platelets in patients leading to low platelet counts and bleeding. The diagnosis and effective management of ITP are challenging because there is no established test to [...] Read more.
Primary Immune Thrombocytopenia (ITP) is a rare autoimmune disease characterised by the immune-mediated destruction of peripheral blood platelets in patients leading to low platelet counts and bleeding. The diagnosis and effective management of ITP are challenging because there is no established test to confirm the disease and no biomarker with which one can predict the response to treatment and outcome. In this work, we conduct a feasibility study to check if machine learning can be applied effectively for the diagnosis of ITP using routine blood tests and demographic data in a non-acute outpatient setting. Various ML models, including Logistic Regression, Support Vector Machine, k-Nearest Neighbor, Decision Tree and Random Forest, were applied to data from the UK Adult ITP Registry and a general haematology clinic. Two different approaches were investigated: a demographic-unaware and a demographic-aware one. We conduct extensive experiments to evaluate the predictive performance of these models and approaches, as well as their bias. The results revealed that Decision Tree and Random Forest models were both superior and fair, achieving nearly perfect predictive and fairness scores, with platelet count identified as the most significant variable. Models not provided with demographic information performed better in terms of predictive accuracy but showed lower fairness scores, illustrating a trade-off between predictive performance and fairness. Full article
(This article belongs to the Special Issue Information-Driven Computer-Aided Diagnosis and Decision Support System)
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