Advances in Statistical Methods and Machine Learning for Medical and Genetic Epidemiology

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 586

Special Issue Editor

Statistics, Department of Mathematics, School of Science and Technology, Nottingham Trent University, Nottingham, UK
Interests: biostatistics

Special Issue Information

Dear Colleagues,

The Special Issue on "Advances in Statistical Methods and Machine Learning for Medical and Genetic Epidemiology" aims to showcase cutting-edge developments in statistical and computational approaches for analyzing complex biomedical and genetic data. This Special Issue highlights the integration of machine learning and artificial intelligence techniques with traditional epidemiological methods to enhance our understanding of disease etiology, risk factors, and health trajectories across the human lifespan.

Key focus areas include the following:

  • Novel machine learning algorithms for handling high-dimensional genetic and biomedical data;
  • Advanced statistical methods for causal inference in observational studies;
  • Innovative approaches to integrate multiple data types, including genomic, environmental, and clinical data;
  • Improved predictive modeling techniques for disease risk and prognosis;
  • Methods for analyzing large-scale population-based studies and biobanks.

This Special Issue brings together contributions from researchers focusing on epidemiology, biostatistics, genetics, and computer science to address challenges in analyzing complex biological systems and heterogeneous datasets.

It aims to provide insights into the application of these advanced methods in real-world biomedical scenarios, from identifying genetic risk factors to developing personalized interventions.

Dr. Ayse Ulgen
Guest Editor

Manuscript Submission Information

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Keywords

  • machine learning
  • artificial intelligence
  • genetic epidemiology
  • causal inference
  • predictive modeling
  • big-data analytics
  • biostatistics
  • precision medicine

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Published Papers (1 paper)

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Research

32 pages, 657 KB  
Article
Debiased Maximum Likelihood Estimators of Hazard Ratios Under Kernel-Based Machine Learning Adjustment
by Takashi Hayakawa and Satoshi Asai
Mathematics 2025, 13(19), 3092; https://doi.org/10.3390/math13193092 - 26 Sep 2025
Viewed by 183
Abstract
Previous studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the unspecified baseline hazard of the model fails to identify temporal change in the risk-set composition due to treatment assignment and unobserved factors among multiple [...] Read more.
Previous studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the unspecified baseline hazard of the model fails to identify temporal change in the risk-set composition due to treatment assignment and unobserved factors among multiple contradictory scenarios. To alleviate this problem, especially in studies based on observational data with uncontrolled dynamic treatment and real-time measurement of many covariates, we propose abandoning the baseline hazard and using kernel-based machine learning to explicitly model the change in the risk set with or without latent variables. For this framework, we clarify the context in which hazard ratios can be causally interpreted, then develop a method based on Neyman orthogonality to compute debiased maximum likelihood estimators of hazard ratios, proving necessary convergence results. Numerical simulations confirm that the proposed method identifies the true hazard ratios with minimal bias. These results lay the foundation for the development of a useful alternative method for causal inference with uncontrolled, observational data in modern epidemiology. Full article
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