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Diagnostics
Special Issues
Artificial Intelligence for Clinical Diagnostic Decision Making
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Artificial Intelligence for Clinical Diagnostic Decision Making

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 January 2025 | Viewed by 547

Special Issue Editors

Dr. Md Mohaimenul Islam

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Guest Editor
College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
Interests: artificial intelligence; health informatics, big data; health equity; medication adherence
Special Issues, Collections and Topics in MDPI journals
Dr. Ming-Chin Lin

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Guest Editor
Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
Interests: artificial intelligence; machine learning; deep learning; electronic health record; patient safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is revolutionizing clinical decision making by offering advanced algorithms and machine learning techniques to analyze vast amounts of medical data and assist healthcare professionals in making accurate and timely diagnoses. AI systems can integrate various types of data, including patient medical history, imaging findings, and genomic information, to provide personalized and evidence-based recommendations. By leveraging AI for clinical decision making, healthcare providers can improve diagnostic accuracy, streamline workflows, and enhance patient outcomes. In this Special Issue, we aim to highlight the latest advancements and innovations in this rapidly evolving field. We invite submissions presenting original research and review articles that explore the application of AI models in diagnosing medical conditions, refining clinical decision-making processes, and ultimately improving patient care outcomes. This Special Issue will serve as a platform to showcase groundbreaking research and foster dialogue among researchers, clinicians, and healthcare policymakers.

Dr. Md Mohaimenul Islam
Dr. Ming-Chin Lin
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

  • artificial intelligence
  • machine learning
  • clinical decision support systems
  • health disparity
  • medical imaging
  • disease diagnosis
  • medical errors
  • patient safety

Published Papers (1 paper)

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13 pages, 639 KiB  
Systematic Review
Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury: A Systematic Review
by Chieh-Chen Wu, Tahmina Nasrin Poly, Yung-Ching Weng, Ming-Chin Lin and Md. Mohaimenul Islam
Diagnostics 2024, 14(15), 1594; https://doi.org/10.3390/diagnostics14151594 - 24 Jul 2024
Viewed by 366
Abstract
While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence [...] Read more.
While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence to determine the effectiveness of ML algorithms for predicting mortality in patients with sepsis-associated AKI. An exhaustive literature search was conducted across several electronic databases, including PubMed, Scopus, and Web of Science, employing specific search terms. This review included studies published from 1 January 2000 to 1 February 2024. Studies were included if they reported on the use of ML for predicting mortality in patients with sepsis-associated AKI. Studies not written in English or with insufficient data were excluded. Data extraction and quality assessment were performed independently by two reviewers. Five studies were included in the final analysis, reporting a male predominance (>50%) among patients with sepsis-associated AKI. Limited data on race and ethnicity were available across the studies, with White patients comprising the majority of the study cohorts. The predictive models demonstrated varying levels of performance, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.60 to 0.87. Algorithms such as extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) showed the best performance in terms of accuracy. The findings of this study show that ML models hold immense ability to identify high-risk patients, predict the progression of AKI early, and improve survival rates. However, the lack of fairness in ML models for predicting mortality in critically ill patients with sepsis-associated AKI could perpetuate existing healthcare disparities. Therefore, it is crucial to develop trustworthy ML models to ensure their widespread adoption and reliance by both healthcare professionals and patients. Full article
(This article belongs to the Special Issue Artificial Intelligence for Clinical Diagnostic Decision Making)
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