Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.0
Journals
Diagnostics
Special Issues
Application of Artificial Intelligence to Advance Individualized Diagnosis and Treatment...
share announcement

Application of Artificial Intelligence to Advance Individualized Diagnosis and Treatment in Emergency and Critical Care Medicine, 2nd Edition

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 November 2025 | Viewed by 3011

Special Issue Editor

Dr. Zhongheng Zhang

E-Mail Website
Guest Editor
Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
Interests: emergency and critical care medicine; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Analytics based on artificial intelligence have greatly advanced scientific research fields such as natural language processing and imaging classification. Clinical research has also been greatly influenced by artificial intelligence for diagnostics involving imaging and molecular markers. Emergency and critical care medicine faces patients with rapidly changing conditions, which require the accurate risk stratification and initiation of rescuing therapies. The key to the successful treatment of critically ill patients involves all aspects of diagnostics such as the early prediction of adverse events, correct diagnosis of causative agents and a differential diagnosis for a manifestation. Critically ill patients usually generate a large amount of data from medical devices such as bedside monitors, ventilators and renal replacement therapy devices. Such large volumes of data are difficult for humans to handle. Artificial intelligence can learn complex data structures to obtain knowledge and wisdom and, thus, can have profound impacts on the management of critically ill patients. Furthermore, critically ill patients such as those with sepsis, acute respiratory distress syndrome and trauma comprise a heterogenous population. The “one-size-fit-all” paradigm may not be suitable for the management of such a heterogeneous patient population. Thus, tools from artificial intelligence can be employed to identify novel subphenotypes of these patients. These subclassifications can not only provide prognostic value for risk stratification but also have predictive value for individualized treatment. Transcriptomes can also provide large amounts of information for the patients; artificial intelligence can greatly help us to obtain useful information from such highly dimensional data.

This Special Issue welcomes articles addressing, but not limited to, the following specific topics:

    Predictive analytics for the risk stratification of emergency and critically ill patients.

    Diagnostics for critical syndromes such as sepsis, acute respiratory failure and acute circulatory failure.

    Biomarkers for the differentiation of critical syndromes.

    Individualized treatment strategies for patients with rapidly changing conditions.

    Subphenotypes of heterogenous populations in emergency and critical care settings.

    Bioinformatics analysis with transcriptomes to develop individualized management.

Reviews, original articles and secondary analyses are welcome.

Dr. Zhongheng Zhang
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. 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

  • critical care
  • sepsis
  • artificial intelligence
  • predictive analytics
  • clustering
  • acute kidney injury
  • emergency medicine
  • transcriptome

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

11 pages, 1135 KiB  
Article
Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction
by Maria Vittoria Chiaruttini, Giulia Lorenzoni, Marco Daverio, Luca Marchetto, Francesca Izzo, Giovanna Chidini, Enzo Picconi, Claudio Nettuno, Elisa Zanonato, Raffaella Sagredini, Emanuele Rossetti, Maria Cristina Mondardini, Corrado Cecchetti, Pasquale Vitale, Nicola Alaimo, Denise Colosimo, Francesco Sacco, Giulia Genoni, Daniela Perrotta, Camilla Micalizzi, Silvia Moggia, Giosuè Chisari, Immacolata Rulli, Andrea Wolfler, Angela Amigoni and Dario Gregoriadd Show full author list remove Hide full author list
Diagnostics 2024, 14(24), 2857; https://doi.org/10.3390/diagnostics14242857 - 19 Dec 2024
Viewed by 853
Abstract
Background/Objectives: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. However, the timely identification of NIV failure is crucial to prevent adverse outcomes. [...] Read more.
Background/Objectives: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. However, the timely identification of NIV failure is crucial to prevent adverse outcomes. This study aims to identify predictors of first-attempt NIV failure in PICU patients by testing various machine learning techniques and comparing their predictive abilities. Methods: Data were sourced from the TIPNet registry, which comprised patients admitted to 23 Italian Paediatric Intensive Care Units (PICUs). We selected patients between January 2010 and January 2024 who received non-invasive ventilation (NIV) as their initial approach to respiratory support. The study aimed to develop a predictive model for NIV failure, selecting the best Machine Learning technique, including Generalized Linear Models, Random Forest, Extreme Gradient Boosting, and Neural Networks. Additionally, an ensemble approach was implemented. Model performances were measured using sensitivity, specificity, AUROC, and predictive values. Moreover, the model calibration was evaluated. Results: Out of 43,794 records, 1861 admissions met the inclusion criteria, with 678 complete cases and 97 NIV failures. The RF model demonstrated the highest AUROC and sensitivity equal to 0.83 (0.64, 0.94). Base excess, weight, age, systolic blood pressure, and fraction of inspired oxygen were identified as the most predictive features. A check for model calibration ensured the model’s reliability in predicting NIV failure probabilities. Conclusions: This study identified highly sensitive models for predicting NIV failure in PICU patients, with RF as a robust option. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence to Advance Individualized Diagnosis and Treatment in Emergency and Critical Care Medicine, 2nd Edition)
►▼ Show Figures

Graphical abstract

40 pages, 13301 KiB  
Article
Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies
by Santino Nanini, Mariem Abid, Yassir Mamouni, Arnaud Wiedemann, Philippe Jouvet and Stephane Bourassa
Diagnostics 2024, 14(23), 2763; https://doi.org/10.3390/diagnostics14232763 - 8 Dec 2024
Cited by 1 | Viewed by 1421
Abstract
Background/Objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from medical-grade sensors. Methods: Tree-based models (TBMs) such as XGBoost, LightGBM, CatBoost, Random Forests (RFs), [...] Read more.
Background/Objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from medical-grade sensors. Methods: Tree-based models (TBMs) such as XGBoost, LightGBM, CatBoost, Random Forests (RFs), Voting Classifier ensembles, and sequential models (LSTM, GRU) were trained on the MIMIC-III and IV datasets. A preprocessing pipeline addressed missing data, class imbalances, and synthetic data flagged with masks. Models were evaluated using a 5-min prediction window with minute-level interpolations for timely interventions. Results: TBMs outperformed sequential models in speed, interpretability, and reliability, making them better suited for real-time decision-making. Feature importance analysis identified six key physiological variables from the enhanced NEWS2+ score and emphasized the value of mask and score features for transparency. Voting Classifier ensembles showed slight metric gains but did not outperform individually optimized models, facing a precision-sensitivity tradeoff and slightly lower F1-scores for key severity levels. Conclusions: TBMs were effective for real-time hypoxemia prediction, while sequential models, though better at temporal handling, were computationally costly. This study highlights ML’s potential to improve triage systems and reduce alarm fatigue, with future plans to incorporate multi-hospital datasets for broader applicability. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence to Advance Individualized Diagnosis and Treatment in Emergency and Critical Care Medicine, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop