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Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort

Genomics and Informatics 2025年第1期23卷 1-11页

作者： Ndagijimana, Frank Aimee Rodrigue Park, Taesung Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea Department of Statistics Seoul National University Seoul South Korea

Background: Understanding the progression and recovery process of COVID-19 is crucial for guiding public health strategies and developing targeted interventions. This longitudinal cohort study aims to elucidate the dynamics of COVID-19 severity progression and evaluate the impact of underlying health conditions on these transitions, providing critical insights for more effective disease management. Methods: Data from 4549 COVID-19 patients admitted to Seoul National University Boramae Medical Center between February 5th, 2020, and October 30th, 2021, were analyzed using a 5-state continuous-time Markov multistate model. The model estimated instantaneous transition rates between different levels of COVID-19 severity, predicted probabilities of state transitions, and determined hazard ratios associated with underlying comorbidities. Results: The analysis revealed that most patients stabilized in their initial state, with 72.2% of patients with moderate symptoms remaining moderate. Patients with hypertension had a 67.6% higher risk of progressing from moderate to severe, while those with diabetes had an 89.9% higher risk of deteriorating from severe to critical. Although transition rates to death were low early in hospitalization, these comorbidities significantly increased the likelihood of worsening conditions. Conclusion: This study highlights the utility of continuous-time Markov multistate models in assessing COVID-19 severity progression among hospitalized patients. The findings indicate that patients are more likely to recover than to experience worsening conditions. However, hypertension and diabetes significantly increase the risk of severe outcomes, underscoring the importance of managing these conditions in COVID-19 patients. © The Author(s) 2025.

关键词： Comorbidity COVID-19 progression Markov multistate model Severity state Transition intensity

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Fetal Health Risk Classification Using Important Feature Selection and Cart Model on Cardiotocography Data

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 193-206页

作者： Ilham, Ahmad Nagara, Thahta Ardhika Prabu Kamaruddin, Mudyawati Khikmah, Laelatul Mantoro, Teddy Department of Informatics Universitas Muhammadiyah Semarang Semarang Indonesia Intelligent Data Science Research Group Universitas Muhammadiyah Semarang Semarang Indonesia Postgraduate Program of Medical Laboratory Science Universitas Muhammadiyah Semarang Semarang Indonesia Interdisciplinary Research Laboratory for Experimental Plasma Medicine Universitas Muhammadiyah Semarang Semarang Indonesia Department of Statistics Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang Semarang Indonesia School of Computer Science Nusa Putra University West Java Indonesia

Fetal mortality and newborn health issues require urgent attention because of high maternal and infant mortality rates during labor, highlighting the critical need for accurate fetal condition monitoring to reduce complications. This study proposes the development of a fetal health risk classification model based on Important Feature Selection (IFS) and a Classification and Regression Tree (CART) using cardiotocography (CTG) data from the UCI Machine Learning Repository. The IFS method was used to select the most relevant features, reduce model complexity, and increase generalization to prevent overfitting. The IFS-CART model was tested with 10-fold cross-validation and showed an accuracy of 94.50%, superior to the conventional CART, which only reached 93.83%. In addition, the average values of the True Positive Rate (TPR) and True Negative Rate (TNR) also increased, indicating that this model is effective in distinguishing normal, suspected, and pathological fetal conditions. Evaluation using the area under the curve receiver operating characteristic (AUC-ROC) showed that the model had high performance in detecting at-risk conditions, with an AUC of 0.981 for the"suspect" class. This finding confirmed that IFS-CART is not only accurate but also has high interpretability, making it easy for medical personnel to use for clinical decision support. The results of this study show that IFS-CART can serve as a reliable decision support system for real-time fetal health monitoring. Further implementation is expected to improve diagnostic accuracy and prevent complications during pregnancy and labor © 2025 Slovene Society Informatika. All rights reserved.

关键词： Fetal monitoring

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Correction: Learning distribution-free anchored linear structural equation models in the presence of measurement error

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Journal of the Korean Statistical Society 2025年 1-2页

作者： Chung, Junhyoung Ahn, Youngmin Shin, Donguk Park, Gunwoong Department of Statistics Seoul National University Seoul Republic of Korea Institute for Data Innovation in Science Seoul National University Donguk Shin Seoul Republic of Korea Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Republic of Korea

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PINT: Physics-Informed Neural Time Series Models with Applications to Long-term Inference on WeatherBench 2m-Temperature Data

arXiv

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arXiv 2025年

作者： Park, Keon Vin Kim, Jisu Seo, Jaemin Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of Department of Statistics Seoul National University Seoul Korea Republic of Department of Physics Chung-Ang University Seoul Korea Republic of

This paper introduces PINT (Physics-Informed Neural Time Series Models), a novel framework designed to integrate physical constraints into neural time series models, thereby enhancing their ability to capture complex dynamics in real-world datasets. To demonstrate its practical utility, we apply PINT to the ERA5 WeatherBench dataset, a widely-used benchmark for climate prediction, focusing on long-term forecasting of 2m-temperature data. PINT leverages the Simple Harmonic Oscillator Equation as a physics-informed prior, incorporating its periodic dynamics into three popular neural architectures: RNN, LSTM, and GRU. The choice of the Simple Harmonic Oscillator Equation is motivated by its well-known analytical solutions (sine and cosine functions), which not only represent periodic dynamics but also enable rigorous evaluation of the performance improvements achieved through the incorporation of physics-informed constraints. By benchmarking against a linear regression baseline derived from the exact solutions of this equation, we quantify the added value of embedding physical principles in data-driven models. Unlike traditional time series approaches that often rely on future observations for inference or training, PINT is designed for practical forecasting scenarios. Using only the first 90 days of observed data, the framework iteratively predicts the next two years, addressing challenges associated with limited or missing real-time updates. Extensive experiments on the WeatherBench dataset showcase PINT’s ability to generalize to unseen data, accurately capture periodic trends, and align with underlying physical principles. This study highlights the potential of physics-informed neural time series models to bridge the gap between data-driven machine learning and the interpretability required for climate applications. The implementation of our models and all the datasets used in this study are publicly available on GitHub: https://***/KV-Park © 2025, CC BY.

关键词： Iterative methods

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Fairness Through Matching

arXiv

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arXiv 2025年

作者： Kim, Kunwoong Kong, Insung Lee, Jongjin Chae, Minwoo Park, Sangchul Kim, Yongdai Department of Statistics Seoul National University Korea Republic of Department of Applied Mathematics University of Twente Netherlands Samsung Research Korea Republic of School of Law Seoul National University Korea Republic of Department of Statistics Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Seoul National University Korea Republic of

Group fairness requires that different protected groups, characterized by a given sensitive attribute, receive equal outcomes overall. Typically, the level of group fairness is measured by the statistical gap between predictions from different protected groups. In this study, we reveal an implicit property of existing group fairness measures, which provides an insight into how the group-fair models behave. Then, we develop a new group-fair constraint based on this implicit property to learn group-fair models. To do so, we first introduce a notable theoretical observation: every group-fair model has an implicitly corresponding transport map between the input spaces of each protected group. Based on this observation, we introduce a new group fairness measure termed Matched Demographic Parity (MDP), which quantifies the averaged gap between predictions of two individuals (from different protected groups) matched by a given transport map. Then, we prove that any transport map can be used in MDP to learn group-fair models, and develop a novel algorithm called Fairness Through Matching (FTM)1, which learns a group-fair model using MDP constraint with an user-specified transport map. We specifically propose two favorable types of transport maps for MDP, based on the optimal transport theory, and discuss their advantages. Experiments reveal that FTM successfully trains group-fair models with certain desirable properties by choosing the transport map accordingly. © 2025, CC BY.

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Avatar influence: How form realism and message appeal shape Generation Z's travel intentions

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Journal of Hospitality and Tourism Management 2025年

作者： Su, Zhen Lei, Biman Liang, Shichang Khotphat, Tanaporn Hu, Qi Business School Guangxi University No. 100 East Daxue Road Nanning 530004 China Guangxi Development Strategy Institute Nanning 530004 China Key Laboratory of Interdisciplinary Science of Statistics and Management Nanning 530004 China Airline Business Program School of Tourism and Hospitality Management Suan Dusit University Bangkok 10300 Thailand

Although avatars have gained popularity in virtual reality marketing, research on their influence on Generation Z consumers' travel intentions remains limited. Drawing on the Construal Level Theory and the Elaboration Likelihood Model, this study examined the congruent effects of avatar form realism and message appeal on Generation Z's travel intentions through three experimental scenarios. The findings indicate that high-form realism avatars with rational appeal and low-form realism avatars with emotional appeal significantly boost travel intentions. These effects are mediated by believability and affect intensity and are more pronounced among individuals with low behavioral realism expectations. This study enhances our understanding of avatar-mediated persuasion in tourism marketing by linking core avatar attributes to consumer decision-making processes. These insights enable us to leverage the visual and verbal components of avatars to promote destinations that attract young travelers. © 2025 The Authors

关键词： Avatar Construal level theory Elaboration Likelihood Model Form realism Generation Z Message appeal

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A linear regression solution to the spatial autocorrelation problem

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Journal of Geographical Systems 2000年第2期2卷 141-156页

作者： Griffith, Daniel A. Department of Geography Interdisciplinary Statistics Program Syracuse University Syracuse NY 13244-1020 United States Natl. Agric. Statistics Service Fairfax VA United States

The Moran Coefficient spatial autocorrelation index can be decomposed into orthogonal map pattern components. This decomposition relates it directly to standard linear regression, in which corresponding eigenvectors can be used as predictors. This paper reports comparative results between these linear regressions and their auto-Gaussian counterparts for the following georeferenced data sets: Columbus (Ohio) crime, Ottawa-Hull median family income, Toronto population density, southwest Ohio unemployment, Syracuse pediatric lead poisoning, and Glasgow standard mortality rates, and a small remotely sensed image of the High Peak district. This methodology is extended to auto-logistic and auto-Poisson situations, with selected data analyses including percentage of urban population across Puerto Rico, and the frequency of SIDs cases across North Carolina. These data analytic results suggest that this approach to georeferenced data analysis offers considerable promise.

关键词： Eigenfunction Geographic weights matrix Georeferenced data Spatial autocorrelation Spatial autoregression

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Confidence Interval Estimation for Machine Learning Models in Forecasting Infectious Diseases

Confidence Interval Estimation for Machine Learning Models i...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Han, Kyulhee Song, Hanbyul Park, Jiwon Liu, Zhe Oh, Jooha Jose, Sayooj Aby Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

Forecasting models have been instrumental in managing the COVID-19 pandemic by facilitating efficient resource distribution and proper interventions. Although various forecasting models - such as mathematical models, statistical models, and machine learning models - are available, a few models account for the uncertainty in their predictions. The lack of uncertainty information constrains the reliability of forecasting results, making them less applicable for use in decision-making. Confidence Intervals (CIs) are widely used in statistical inference to provide uncertainty information. In this study, we introduce a framework that provides bootstrap-based CIs readily applicable to various forecasting models. The key concept of this framework is repeated training on bootstrap data based on resampled residuals from the forecasting model. After fitting the forecasting model, bootstrap datasets are generated considering time series characteristics to retrain the forecasting models. Then, CIs are calculated based on the distribution of prediction values. This framework was applied to forecasting COVID-19 outcomes - such as daily confirmed COVID-19 cases, deaths, and ICU patients - in South Korea. Our results demonstrate that bootstrap-based CIs can be successfully applied to provide additional information on the reliability of prediction. In conclusion, our framework can provide uncertainty information without requiring complex assumptions, regardless of the forecasting model or response variable type. Therefore, this approach can support public health policymakers in gaining a deeper understanding of epidemic trends, thereby offering valuable insights for decision-making. © 2024 IEEE.

关键词： COVID-19

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Uncertainty Quantification and Statistical Inference for Biologically Informed Neural Networks

Uncertainty Quantification and Statistical Inference for Bio...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Lee, Chanhee Shin, Sangyeon Kim, Haeyoung Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

In precision medicine, deep learning models play an important role in identifying relevant biomarkers and making predictions for the early diagnosis and prognosis of diseases. While successful in developing accurate prediction models for prognosis and diagnosis, the 'black box' nature of deep learning poses significant challenges, which makes it difficult to have a meaningful biological interpretation. Recently, several biologically informed neural networks (BINNs), such as the Visible Neural Network and P-NET, have been proposed to incorporate biological knowledge for improved interpretation. These models assess node importance using quantitative measures such as SHAP and DeepLIFT. However, these approaches have limitations because they typically provide a single estimate of node importance without any information on the uncertainty of these estimates. This study aims to assess uncertainty of node importance through a novel statistical framework. Bootstrapping is used to calculate confidence interval of node importance. Our findings validate that the proposed statistical framework quantifies the uncertainty of nodes in BINNs. By providing robust confidence intervals of pathways and genes, this framework enhances the interpretability and reliability of BINNs. © 2024 IEEE.

关键词： Neural network models

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Genome-wide association analysis with matched samples discloses additional novel risk loci

Genome-wide association analysis with matched samples disclo...

引用

IEEE International Conference on Bioinformatics and Biomedicine

作者： Jungsoo Gim Sungkyoung Choi Jongho Im Jae-Kwang Kim Taesung Park Interdisciplinary program for Bioinformatics and Department of Statistics Seoul National University Interdisciplinary program for Bioinformatics Seoul National University Department of Statistics Iowa State University

ISBN: (纸本)9781479956708

Genome-wide association studies have identified many candidate causal variants associated with common complex diseases and traits, but most of them have been drawn from nonrandomized case/control designs. In nonrandomized experiments, the results drawn from two different groups can be misleading because the units exposed to one group generally differ systematically from the units exposed to the other group. Propensity score is widely used to group case and control units for a more direct and significant comparison even with nonrandomized experiments. This propensity score matching can help with prioritizing additional uncovered variants on disease risk via sub-group analysis in genome-wide association studies. The aim of this work is to propose a post-hoc association test based on the subsets of samples. For that purpose, this paper presents a new paradigm for a post-hoc genome-wide association test when the sample size of controls are larger than that of cases: selecting control samples by equating the distribution of covariates in the case and control groups and re-performing association analysis upon these matched samples. We demonstrated the feasibility of this approach by applying it to 2752 type II diabetes patients in 8842 Korean population. Genome-wide association approach with matched samples is able to disclose 9 additional novel variants and 7 out of 9 have not identified from the association test of whole control samples. The process described here can successfully be combined with other types of case/control studies with large covariate information. This indicates that there a possibility of obtaining additional candidate causal variants responsible for common diseases through genome-wide association analysis with matched samples.

关键词： Propensity score Matching Genome-wide association study

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