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Machine Learning in Epidemiology
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Machine Learning in Epidemiology

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Epidemiology".

Deadline for manuscript submissions: closed (30 May 2025) | Viewed by 897

Special Issue Editors

Dr. Xingyu Zhang

E-Mail Website
Guest Editor
School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA 15260, USA
Interests: biomedical and clinical research with a strong focus on study design and statistical analysis; physiological data analytics; electronic health record data mining; predictive modeling and machine learning in biomedical science
Dr. Leming Zhou

E-Mail Website
Guest Editor
Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA 15260, USA
Interests: mobile health and telehealth system development and evaluation; machine learning; large-scale data analytics; information security and privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) are revolutionizing epidemiology, offering unprecedented tools for analyzing vast amounts of health data and generating previously unobtainable insights. These technologies enable the development of predictive models, improve disease surveillance, and optimize healthcare resource allocation. AI/ML approaches also encourage personalized healthcare by tailoring interventions based on individual risk factors and population-level trends, thus enhancing both prevention and treatment strategies. 

In this Special Issue, we invite submissions that explore the innovative use of AI and ML in epidemiological research, with a particularly interest in how these methodologies can be leveraged to improve disease outbreak predictions, support public health decision making, and utilize both structured and unstructured data to achieve more precise health outcomes. Additionally, we welcome research that highlights the challenges, ethical considerations, and future directions in AI/ML integration in the field of epidemiology. 

By fostering interdisciplinary collaboration and advancing AI/ML applications, this Special Issue aims to contribute to the development of more effective, data-driven public health strategies essential for addressing current and emerging health challenges. 

Dr. Xingyu Zhang
Dr. Leming Zhou
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. Journal of Personalized Medicine 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 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
  • epidemiology
  • predictive analytics
  • health data
  • public health
  • precision medicine
  • disease surveillance
  • big data analytics

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Published Papers (1 paper)

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Research

15 pages, 2070 KiB  
Article
Machine Learning for Personalized Prediction of Electrocardiogram (EKG) Use in Emergency Care
by Hairong Wang and Xingyu Zhang
J. Pers. Med. 2025, 15(8), 358; https://doi.org/10.3390/jpm15080358 - 6 Aug 2025
Viewed by 223
Abstract
Background: Electrocardiograms (EKGs) are essential tools in emergency medicine, often used to evaluate chest pain, dyspnea, and other symptoms suggestive of cardiac dysfunction. Yet, EKGs are not universally administered to all emergency department (ED) patients. Understanding and predicting which patients receive an [...] Read more.
Background: Electrocardiograms (EKGs) are essential tools in emergency medicine, often used to evaluate chest pain, dyspnea, and other symptoms suggestive of cardiac dysfunction. Yet, EKGs are not universally administered to all emergency department (ED) patients. Understanding and predicting which patients receive an EKG may offer insights into clinical decision making, resource allocation, and potential disparities in care. This study examines whether integrating structured clinical data with free-text patient narratives can improve prediction of EKG utilization in the ED. Methods: We conducted a retrospective observational study to predict electrocardiogram (EKG) utilization using data from 13,115 adult emergency department (ED) visits in the nationally representative 2021 National Hospital Ambulatory Medical Care Survey–Emergency Department (NHAMCS-ED), leveraging both structured features—demographics, vital signs, comorbidities, arrival mode, and triage acuity, with the most influential selected via Lasso regression—and unstructured patient narratives transformed into numerical embeddings using Clinical-BERT. Four supervised learning models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGB)—were trained on three inputs (structured data only, text embeddings only, and a late-fusion combined model); hyperparameters were optimized by grid search with 5-fold cross-validation; performance was evaluated via AUROC, accuracy, sensitivity, specificity and precision; and interpretability was assessed using SHAP values and Permutation Feature Importance. Results: EKGs were administered in 30.6% of adult ED visits. Patients who received EKGs were more likely to be older, White, Medicare-insured, and to present with abnormal vital signs or higher triage severity. Across all models, the combined data approach yielded superior predictive performance. The SVM and LR achieved the highest area under the ROC curve (AUC = 0.860 and 0.861) when using both structured and unstructured data, compared to 0.772 with structured data alone and 0.823 and 0.822 with unstructured data alone. Similar improvements were observed in accuracy, sensitivity, and specificity. Conclusions: Integrating structured clinical data with patient narratives significantly enhances the ability to predict EKG utilization in the emergency department. These findings support a personalized medicine framework by demonstrating how multimodal data integration can enable individualized, real-time decision support in the ED. Full article
(This article belongs to the Special Issue Machine Learning in Epidemiology)
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