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Bioengineering
Special Issues
Machine Learning-Driven Innovations in Predictive Healthcare
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Machine Learning-Driven Innovations in Predictive Healthcare

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 541

Special Issue Editors

Dr. Julfikar Haider

E-Mail Website
Guest Editor
Department of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
Interests: composites in dentistry; tribology; surface engineering; metal matrix composites; fiber-reinforced polymer composites; nanocomposites
Special Issues, Collections and Topics in MDPI journals
Dr. Sang Won Park

E-Mail Website
Guest Editor
Department of Next Generation Information Center, Kangwon National University Hospital, Chuncheon 24289, Republic of Korea
Interests: clinical data analysis; digital healthcare; machine learning; neuroscience; deep learning
Dr. Mominul Ahsan

E-Mail Website
Guest Editor
Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK
Interests: artificial intelligence; machine learning; data analytics; medical image analysis; prognostics; data-driven predictive models; wireless sensor data communication; wearable technology; Industry 4.0

Special Issue Information

Dear Colleagues,

This Special Issue, titled "Machine Learning-Driven Innovations in Predictive Healthcare", aims to provide a platform for researchers to showcase their latest research and findings on the application of machine learning and deep learning technologies in clinical and healthcare settings. With the rapid advancement of digital healthcare systems and the increasing availability of large-scale clinical datasets, machine learning and deep learning approaches have emerged as powerful tools that can revolutionize multiple areas of healthcare, including disease prediction, personalized treatment planning, and neurological disorder analysis.

This Special Issue will cover a wide range of topics related to the application of machine learning and deep learning in clinical data analysis and neuroscience, including predictive modeling, patient outcome forecasting, medical imaging and signal interpretation, healthcare data integration, and decision support systems. Contributions from interdisciplinary teams that can integrate clinical medicine, digital health technologies, neuroscience, and computational modeling are highly encouraged.

Dr. Julfikar Haider
Dr. Sang Won Park
Dr. Mominul Ahsan
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 250 words) can be sent to the Editorial Office for assessment.

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. Bioengineering 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 2700 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

  • machine learning
  • clinical data analysis
  • medical decision support
  • disease prediction
  • personalized medicine

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

Published Papers (1 paper)

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14 pages, 23445 KB  
Article
A Machine Learning-Based Clinical Decision Support Tool for Intertrochanteric Hip Fracture Patients to Predict Postoperative Anemia Risk: A Retrospective Cohort Study
by Xinbei Dong, Qinglong Wang, Zhipeng Huang and Yucai Wang
Bioengineering 2026, 13(5), 489; https://doi.org/10.3390/bioengineering13050489 - 23 Apr 2026
Abstract
Background: Postoperative anemia associated with intertrochanteric hip fracture is a detrimental complication that detrimentally impairs patients’ outcomes. This study is designed to develop an online predictive tool to assist physicians in developing surgical blood preparation strategies to prevent the occurrence of postoperative anemia. [...] Read more.
Background: Postoperative anemia associated with intertrochanteric hip fracture is a detrimental complication that detrimentally impairs patients’ outcomes. This study is designed to develop an online predictive tool to assist physicians in developing surgical blood preparation strategies to prevent the occurrence of postoperative anemia. Methods: This study included data collected from June 2017 to June 2025 on intertrochanteric hip fracture patients at Tangdu Hospital, including demographic information, comorbidities, vital signs, and laboratory results. LASSO regression was used to select predictive variables, and seven machine learning techniques: Logistic Regression, Support Vector Machine, Decision Tree, LightGBM, XGBoost, Neural Networks, and Random Forest, were compared to identify the best tool for predicting postoperative anemia risk. We created a patient-specific risk prediction tool with SHAP-driven interpretability for clinical decision support. Results: A total of 815 patients were included in the analysis, of whom 208 (25.5%) presented with postoperative anemia. Eight variables were selected to build seven machine learning models. Among these, the SVM model exhibited the best predictive performance in terms of discrimination, calibration, and clinical applicability, with an AUC range of 0.827–0.831. In test sets encompassing diverse population characteristics, SVM achieved the highest sensitivity (72.73%), accuracy (77.78%), and F1 score (57.14%). Conclusions: We established an online prediction platform for clinical practice, enabling clinicians to assess anemia risk in intertrochanteric hip fracture patients and support early prevention of postoperative anemia. Full article
(This article belongs to the Special Issue Machine Learning-Driven Innovations in Predictive Healthcare)
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