Shengfan Zhang
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Associate Professor
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Shengfan Zhang's research focuses on applying operations research and quantitative methods to healthcare delivery and decision-making. Her work has investigated factors influencing women's attitudes and behaviors toward screening mammography, utilizing design-based logistic regression and heuristic-based regression models to evaluate policies and adaptive decision-making processes. Zhang has also explored the impact of comorbidity on breast cancer patient outcomes. Her research extends to other areas, including the development of predictive models for tuberculosis treatment outcomes using machine learning algorithms and the investigation of neuroinflammatory mechanisms in mouse models of intracerebral hemorrhage. Zhang holds an h-index of 11 with 42 total publications and 287 citations. She has collaborated with researchers including Zephan Wade, Donald G. Catanzaro, and Maryam Kheirandish at the University of Arkansas at Fayetteville.
Metrics
- h-index: 11
- Publications: 42
- Citations: 287
Positions
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Associate Professor 2019–presentUniversity of Arkansas Department of Industrial Engineering ORCID
Selected Publications
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Quantifying uncertainty in deep learning binary classification with discrete noise in inputs for risk-based decision making (2025)
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Effects of magnesium phosphate cement proportions on paste properties (2025)
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SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks (2024)
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Is human perception reliable? Toward illumination robust food freshness prediction from food appearance — Taking lettuce freshness evaluation as an example (2024)
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An Analysis of Data Analytics Curriculum Development through an NSF Research Experience for Teachers (RET) Program in Arkansas (2024)
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Board 362: Promoting Research-Driven Data Analytics Curriculum in High School through an NSF RET Site (2024)
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System Simulation And Machine Learning-Based Maintenance Optimization For An Inland Waterway Transportation System (2023)
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Erlang loss systems with shortest idle server first service discipline: Maintenance considerations (2023)
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Utilizing Clinical Trial Data to Assess Timing of Surgical Treatment for Emphysema Patients (2022)
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Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes (2022)
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Establishing a Research Experience for Teachers Site to Enhance Data Analytics Curriculum in Secondary STEM Education (2021)
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Evaluating Risk-Stratified HPV Catch-up Vaccination Strategies: Should We Go beyond Age 26? (2021)
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Effective resource utilization in Arkansas public schools (2020)
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Methodical analysis of inventory discrepancy under conditions of uncertainty in supply chain management (2019)
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Methodical analysis of inventory discrepancy under conditions of uncertainty in supply chain management (2019)
Collaboration Network
Top Collaborators
- Analyzing factors associated with women’s attitudes and behaviors toward screening mammography using design-based logistic regression
- Evaluation of breast cancer mammography screening policies considering adherence behavior
- Analyzing overdiagnosis risk in cancer screening: A case of screening mammography for breast cancer
- Erlang loss systems with shortest idle server first service discipline: Maintenance considerations
- COST‐EFFECTIVENESS ANALYSIS OF BREAST CANCER MAMMOGRAPHY SCREENING POLICIES CONSIDERING UNCERTAINTY IN WOMEN'S ADHERENCE
- The association of breast density with breast cancer mortality in African American and white women screened in community practice
- Characterizing the impact of mental disorders on HIV patient length of stay and total charges
- Competing risks analysis in mortality estimation for breast cancer patients from independent risk groups
- Analyzing factors associated with women’s attitudes and behaviors toward screening mammography using design-based logistic regression
- Evaluation of breast cancer mammography screening policies considering adherence behavior
- Adaptive decision-making of breast cancer mammography screening: A heuristic-based regression model
- Establishing a Research Experience for Teachers Site to Enhance Data Analytics Curriculum in Secondary STEM Education
- Board 362: Promoting Research-Driven Data Analytics Curriculum in High School through an NSF RET Site
- An Analysis of Data Analytics Curriculum Development through an NSF Research Experience for Teachers (RET) Program in Arkansas
- Characterizing the impact of mental disorders on HIV patient length of stay and total charges
- Inferring breast cancer concomitant diagnosis and comorbidities from the Nationwide Inpatient Sample using social network analysis
- The association of breast density with breast cancer mortality in African American and white women screened in community practice
- Competing risks analysis in mortality estimation for breast cancer patients from independent risk groups
- The association of breast density with breast cancer mortality in African American and white women screened in community practice
- Competing risks analysis in mortality estimation for breast cancer patients from independent risk groups
- Strategic level proton therapy patient admission planning: a Markov decision process modeling approach
- Analyzing overdiagnosis risk in cancer screening: A case of screening mammography for breast cancer
- Quantifying the benefits of continuous replenishment program for partner evaluation
- Analyzing overdiagnosis risk in cancer screening: A case of screening mammography for breast cancer
- Analyzing overdiagnosis risk in cancer screening: A case of screening mammography for breast cancer
- Erlang loss systems with shortest idle server first service discipline: Maintenance considerations
- Methodical analysis of inventory discrepancy under conditions of uncertainty in supply chain management
- Methodical analysis of inventory discrepancy under conditions of uncertainty in supply chain management
- Front Matter
- INDEX
- Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes
- Quantifying uncertainty in deep learning binary classification with discrete noise in inputs for risk-based decision making
- Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes
- Quantifying uncertainty in deep learning binary classification with discrete noise in inputs for risk-based decision making
- Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes
- Quantifying uncertainty in deep learning binary classification with discrete noise in inputs for risk-based decision making
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