Research Areas
Biomedical Subjects
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Biography and Research Information
OverviewAI-generated summary
Maryam Kheirandish's research focuses on the application of machine learning algorithms to predict and analyze health outcomes, particularly in the context of tuberculosis treatment. She has investigated methods for integrating dynamic modeling frameworks with machine learning to forecast treatment success. Her work also addresses the quantification of uncertainty within deep learning models, specifically when dealing with noise in discrete input data, which is crucial for risk-based decision-making in healthcare. Kheirandish has published three scholarly articles and has a citation count of 23, with an h-index of 2. Her collaborations include Shengfan Zhang and Donald G. Catanzaro, both at the University of Arkansas at Fayetteville, with whom she has co-authored three publications.
Metrics
- h-index: 2
- Publications: 3
- Citations: 23
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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Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes (2022)
Collaboration Network
Top Collaborators
- 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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