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Your search keyword '"Zhang, Qingpeng"' showing total 439 results
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439 results on '"Zhang, Qingpeng"'

2. Predicting the Risk of Ischemic Stroke in Patients with Atrial Fibrillation using Heterogeneous Drug-protein-disease Network-based Deep Learning

Author

Lyu, Zhiheng, Yang, Jiannan, Xu, Zhongzhi, Wang, Weilan, Cheng, Weibin, Tsui, Kwok-Leung, Tse, Gary, and Zhang, Qingpeng

Subjects
Quantitative Biology - Quantitative Methods
Abstract

We develop a deep learning model, ABioSPATH, to predict the one-year risk of ischemic stroke (IS) in atrial fibrillation (AF) patients. The model integrates drug-protein-disease pathways and real-world clinical data of AF patients to generate the IS risk and potential pathways for each patient. The model uses a multilayer network to identify the mechanism of drug action and disease comorbidity propagation pathways. The model is tested on the Electronic Health Record (EHR) data of 7859 AF patients from 43 hospitals in Hong Kong. The model outperforms all baselines across all metrics and provides valuable molecular-level insights for clinical use. The model also highlights key proteins in common pathways and potential IS risks tied to less-studied drugs. The model only requires routinely collected data, without requiring expensive biomarkers to be tested.

Published
2024

3. Data organization limits the predictability of binary classification

Author

Jing, Fei, Zhang, Zi-Ke, Zhang, Yi-Cheng, and Zhang, Qingpeng

Subjects
Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Physics - Data Analysis, Statistics and Probability
Abstract

The structure of data organization is widely recognized as having a substantial influence on the efficacy of machine learning algorithms, particularly in binary classification tasks. Our research provides a theoretical framework suggesting that the maximum potential of binary classifiers on a given dataset is primarily constrained by the inherent qualities of the data. Through both theoretical reasoning and empirical examination, we employed standard objective functions, evaluative metrics, and binary classifiers to arrive at two principal conclusions. Firstly, we show that the theoretical upper bound of binary classification performance on actual datasets can be theoretically attained. This upper boundary represents a calculable equilibrium between the learning loss and the metric of evaluation. Secondly, we have computed the precise upper bounds for three commonly used evaluation metrics, uncovering a fundamental uniformity with our overarching thesis: the upper bound is intricately linked to the dataset's characteristics, independent of the classifier in use. Additionally, our subsequent analysis uncovers a detailed relationship between the upper limit of performance and the level of class overlap within the binary classification data. This relationship is instrumental for pinpointing the most effective feature subsets for use in feature engineering., Comment: 98 pages, 69 figures

Published
2024

6. Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria

Author

Li, Yijun, Guo, Mengzhuo, Kadziński, Miłosz, and Zhang, Qingpeng

Subjects
Computer Science - Machine Learning
Abstract

The advent of predictive methodologies has catalyzed the emergence of data-driven decision support across various domains. However, developing models capable of effectively handling input time series data presents an enduring challenge. This study presents novel preference learning approaches to multiple criteria sorting problems in the presence of temporal criteria. We first formulate a convex quadratic programming model characterized by fixed time discount factors, operating within a regularization framework. To enhance scalability and accommodate learnable time discount factors, we introduce a novel monotonic Recurrent Neural Network (mRNN). It is designed to capture the evolving dynamics of preferences over time while upholding critical properties inherent to MCS problems, including criteria monotonicity, preference independence, and the natural ordering of classes. The proposed mRNN can describe the preference dynamics by depicting marginal value functions and personalized time discount factors along with time, effectively amalgamating the interpretability of traditional MCS methods with the predictive potential offered by deep preference learning models. Comprehensive assessments of the proposed models are conducted, encompassing synthetic data scenarios and a real-case study centered on classifying valuable users within a mobile gaming app based on their historical in-app behavioral sequences. Empirical findings underscore the notable performance improvements achieved by the proposed models when compared to a spectrum of baseline methods, spanning machine learning, deep learning, and conventional multiple criteria sorting approaches.

Published
2023

12. An online advertising intervention to increase adherence to stay-at-home-orders during the COVID-19 pandemic: An efficacy trial monitoring individual-level mobility data

Author

Garett, Renee R, Yang, Jiannan, Zhang, Qingpeng, and Young, Sean D

Subjects
Physical Geography and Environmental Geoscience, Environmental Management, Earth Sciences, Engineering, Environmental Sciences, Geomatic Engineering, Clinical Trials and Supportive Activities, Behavioral and Social Science, Clinical Research, Patient Safety, Prevention, Management of diseases and conditions, 7.1 Individual care needs, Good Health and Well Being, Digital health, Intervention, COVID-19, Mobility, Artificial intelligence, Geological & Geomatics Engineering, Physical geography and environmental geoscience, Geomatic engineering, Environmental management
Abstract

The COVID-19 pandemic has led public health departments to issue several orders and recommendations to reduce COVID-19-related morbidity and mortality. However, for various reasons, including lack of ability to sufficiently monitor and influence behavior change, adherence to these health orders and recommendations has been suboptimal. Starting April 29, 2020, during the initial stay-at-home orders issued by various state governors, we conducted an intervention that sent online website and mobile application advertisements to people's mobile phones to encourage them to adhere to stay-at-home orders. Adherence to stay-at-home orders was monitored using individual-level cell phone mobility data, from April 29, 2020 through May 10, 2020. Mobile devices across 5 regions in the United States were randomly-assigned to either receive advertisements from our research team advising them to stay at home to stay safe (intervention group) or standard advertisements from other advertisers (control group). Compared to control group devices that received only standard corporate advertisements (i.e., did not receive public health advertisements to stay at home), the (intervention group) devices that received public health advertisements to stay at home demonstrated objectively-measured increased adherence to stay at home (i.e., smaller radius of gyration, average travel distance, and larger stay-at-home ratios). Results suggest that 1) it is feasible to use mobility data to assess efficacy of an online advertising intervention, and 2) online advertisements are a potentially effective method for increasing adherence to government/public health stay-at-home orders.

Published
2022

15. Comparison of Sodium-Glucose Cotransporter-2 Inhibitor and Dipeptidyl Peptidase-4 Inhibitor on the Risks of New-Onset Atrial Fibrillation, Stroke and Mortality in Diabetic Patients: A Propensity Score-Matched Study in Hong Kong

Author

Lee, Sharen, Zhou, Jiandong, Leung, Keith Sai Kit, Wai, Abraham Ka Chung, Jeevaratnam, Kamalan, King, Emma, Liu, Tong, Wong, Wing Tak, Chang, Carlin, Wong, Ian Chi Kei, Cheung, Bernard Man Yung, Tse, Gary, and Zhang, Qingpeng

Published
2023
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17. Cross-platform Product Matching Based on Knowledge Graph

Author

Liu, Wenlong, Pan, Jiahua, Zhang, Xingyu, Gong, Xinxin, Ye, Yang, Zhao, Xujin, Wang, Xin, Wu, Kent, Xiang, Hua, Zhang, Qingpeng, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Yang, Shiyu, editor, and Islam, Saiful, editor

Published
2023
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18. OntoCA: Ontology-Aware Caching for Distributed Subgraph Matching

Author

Qin, Yuzhou, Wang, Xin, Hao, Wenqi, Liu, Pengkai, Song, Yanyan, Zhang, Qingpeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Bohan, editor, Yue, Lin, editor, Tao, Chuanqi, editor, Han, Xuming, editor, Calvanese, Diego, editor, and Amagasa, Toshiyuki, editor

Published
2023
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