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Machine Learning and Data Science in Healthcare
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Machine Learning and Data Science in Healthcare

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 933

Special Issue Editors

Dr. Mohamed Bennasar

E-Mail Website
Guest Editor
School of Computing and Communications, The Open University, Milton Keynes, UK
Interests: AI; machine learning; data science; digital health
Dr. Yutong Xie

E-Mail Website
Guest Editor
Australian Institute for Machine Learning, University of Adelaide, North Terrace 5005, Australia
Interests: computer vision and data analytics in the healthcare sector; smart solutions to assist healthcare professionals in anatomical structure segmentation; disease diagnosis

Special Issue Information

Dear Colleagues,

The Special Issue focuses on the transformative role of data-driven approaches in modern medicine and healthcare delivery. It explores innovative applications of data science, including machine learning, artificial intelligence (AI), and big data analytics, to address critical challenges in healthcare. 

This Special Issue aims to bridge the gap between technology and medicine by highlighting cutting-edge research and practical implementations. It emphasizes the potential of data science to transform healthcare systems, improve patient outcomes, reduce costs, and drive innovation in medical practice. Topics of interest include (but are not limited to) the following:

  1. Predictive and Personalised Medicine:
    • Leveraging predictive analytics to identify disease risks and tailor treatments for individual patients.
  2. AI and Machine Learning in Diagnostics:
    • Applying AI algorithms to enhance diagnostic accuracy, such as image recognition for medical imaging or natural language processing (NLP) for clinical notes.
    • Implementing real-time diagnostic tools to detect early signs of diseases.
  3. Public Health and Population Management:
    • Using data science to model disease outbreaks, optimise resource allocation, and monitor population health trends.
    • Enhancing health equity through insights gained from large-scale health data.
  4. Remote Monitoring and Digital Health:
    • Integration of wearable devices and IoT for continuous health monitoring.
    • Advancing telemedicine platforms to improve access to care, particularly in remote or underserved areas.
  5. Clinical Decision Support Systems:
    • Designing systems to assist healthcare providers in decision-making by providing evidence-based recommendations.
    • Improving patient outcomes through data-driven insights.

Dr. Mohamed Bennasar
Dr. Yutong Xie
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. Information is an international peer-reviewed open access monthly 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 1800 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 (AI) 
  • digital health 
  • diagnostic tools 
  • predictive analytics
  • clinical decision support systems 
  • health data visualisation 
  • machine learning (ML) 
  • precision medicine 
  • telemedicine

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

Published Papers (1 paper)

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Research

15 pages, 1469 KB  
Article
Prediction of Postoperative ICU Requirements: Closing the Translational Gap with a Real-World Clinical Benchmark for Artificial Intelligence Approaches
by Alexander Althammer, Felix Berger, Oliver Spring, Philipp Simon, Felix Girrbach, Maximilian Dieing, Jens O. Brunner, Sergey Shmygalev, Christina C. Bartenschlager and Axel R. Heller
Information 2025, 16(10), 888; https://doi.org/10.3390/info16100888 - 13 Oct 2025
Viewed by 420
Abstract
Background: Accurate prediction of postoperative care requirements is critical for patient safety and resource allocation. Although numerous approaches involving artificial intelligence (AI) and machine learning (ML) have been proposed to support such predictions, their implementation in practice has so far been insufficiently successful. [...] Read more.
Background: Accurate prediction of postoperative care requirements is critical for patient safety and resource allocation. Although numerous approaches involving artificial intelligence (AI) and machine learning (ML) have been proposed to support such predictions, their implementation in practice has so far been insufficiently successful. One reason for this is that the performance of the algorithms is difficult to assess in practical use, as the accuracy of clinical decisions has not yet been systematically quantified. As a result, models are often assessed purely from a technical perspective, neglecting the socio-technical context. Methods: We conducted a retrospective, single-center observational study at the University Hospital Augsburg, including 35,488 elective surgical cases documented between August 2023 and January 2025. For each case, preoperative care-level predictions by surgical and anesthesiology teams were compared with the actual postoperative care provided. Predictive performance was evaluated using accuracy and sensitivity. Since this is a highly imbalanced dataset, in addition to sensitivity and specificity, the balanced accuracy and the Fβ-score were also calculated. The results were contrasted with published Machine-Learning (ML)-based approaches. Results: Overall prediction accuracy was high (surgery: 91.2%; anesthesiology: 87.1%). However, sensitivity for identifying patients requiring postoperative intensive care was markedly lower than reported for ML models in the literature, with the largest discrepancies observed in patients ultimately admitted to the ICU (surgery: 38.05%; anesthesiology: 56.84%; ML: 70%). Nevertheless, clinical judgment demonstrated a superior F1-score, indicating a more balanced performance between sensitivity and precision (surgery: 0.527; anesthesiology: 0.551; ML: 0.28). Conclusions: This study provides the first real-world benchmark of clinical expertise in postoperative care prediction and shows a way in which modern ML approaches must be evaluated in a specific sociotechnical context. By quantifying the predictive performance of surgeons and anesthesiologists, it enables an evaluation of existing ML approaches. Thus the strength of our work is the provision of a real-world benchmark against which all ML methods for preoperative prediction of ICU demand can be systematically evaluated. This enables, for the first time, a comparison of different approaches on a common, practice-oriented basis and thus significantly facilitates translation into clinical practice, thereby closing the translational gap. Furthermore it offers a data-driven framework to support the integration of ML into preoperative decision-making. Full article
(This article belongs to the Special Issue Machine Learning and Data Science in Healthcare)
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