Biography and Research Information
OverviewAI-generated summary
Nahiyan Bin Noor's research focuses on the application of machine learning algorithms to address public health challenges, particularly in the areas of substance use disorder and toxicity detection. He has investigated the use of machine learning for predicting patient retention, overdoses, and mortality among US military veterans undergoing buprenorphine treatment for opioid use disorder. Additionally, his work includes developing and validating algorithms for predicting toxicity in online discourse, such as on Reddit and music lyrics, and comparing toxicity across different social media platforms concerning COVID-19 conversations. Bin Noor also explores the application of machine learning in medical imaging for anemia prediction and integrates machine learning with large language models for heart disease prediction. His scholarship includes 12 publications and has garnered 81 citations, with an h-index of 4. He has collaborated with researchers from the University of Arkansas for Medical Sciences and the University of Arkansas at Little Rock.
Metrics
- h-index: 4
- Publications: 15
- Citations: 90
Selected Publications
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Evaluating the optimal duration of medication treatment for opioid use disorder (2026)
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Association between different modalities of opioid use disorder-related care delivery and opioid use disorder-related patient outcomes: A retrospective cohort study (2025)
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Development and Validation of Machine-Learning Algorithms Predicting Retention, Overdoses, and All-Cause Mortality Among US Military Veterans Treated with Buprenorphine for Opioid Use Disorder (2025)
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Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder (2024)
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Examining Toxicity’s Impact on Reddit Conversations (2024)
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A Systematic Approach to Predict Anemia from Eye Conjunctiva Images (2023)
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Towards Carbon-Neutral Healthcare Facilities: Design and Evaluation of a Renewable Energy Microgrid (2023)
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Strategic Utilization of Dispatchable Loads and Nodal Reserves for Improved Reserve Deliverability (2023)
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An Efficient Technique of Predicting Toxicity on Music Lyrics Machine Learning (2023)
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A Survey on Neural and Non-Neural Network Based Approaches to Classify Images and Signals (2023)
Collaboration Network
Top Collaborators
Corey J. Hayes
Central Arkansas Veterans Healthcare System
3 shared publications
In database
- Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder
- Development and Validation of Machine-Learning Algorithms Predicting Retention, Overdoses, and All-Cause Mortality Among US Military Veterans Treated with Buprenorphine for Opioid Use Disorder
- Association between different modalities of opioid use disorder-related care delivery and opioid use disorder-related patient outcomes: A retrospective cohort study
Niloofar Yousefi
University of Arkansas at Little Rock (US)
2 shared publications
- Examining Toxicity’s Impact on Reddit Conversations
- Comparing Toxicity Across Social Media Platforms for COVID-19 Discourse
Billy Spann
University of Arkansas at Little Rock
2 shared publications
In database
- Examining Toxicity’s Impact on Reddit Conversations
- Comparing Toxicity Across Social Media Platforms for COVID-19 Discourse
Nitin Agarwal
University of Arkansas at Little Rock (US)
2 shared publications
- Examining Toxicity’s Impact on Reddit Conversations
- Comparing Toxicity Across Social Media Platforms for COVID-19 Discourse
Joyonta Das Joy
Chittagong University of Engineering & Technology (BD)
2 shared publications
- Strategic Utilization of Dispatchable Loads and Nodal Reserves for Improved Reserve Deliverability
- Towards Carbon-Neutral Healthcare Facilities: Design and Evaluation of a Renewable Energy Microgrid
Samin Salsabil
Chittagong University of Engineering & Technology (BD)
2 shared publications
- Strategic Utilization of Dispatchable Loads and Nodal Reserves for Improved Reserve Deliverability
- Towards Carbon-Neutral Healthcare Facilities: Design and Evaluation of a Renewable Energy Microgrid
Abtahi Reza
Temple University (US)
2 shared publications
- Strategic Utilization of Dispatchable Loads and Nodal Reserves for Improved Reserve Deliverability
- Towards Carbon-Neutral Healthcare Facilities: Design and Evaluation of a Renewable Energy Microgrid
Rebecca A. Raciborski
Central Arkansas Veterans Healthcare System (US)
2 shared publications
- Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder
- Development and Validation of Machine-Learning Algorithms Predicting Retention, Overdoses, and All-Cause Mortality Among US Military Veterans Treated with Buprenorphine for Opioid Use Disorder
Bradley C. Martin
University of Arkansas for Medical Sciences
2 shared publications
In database
- Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder
- Development and Validation of Machine-Learning Algorithms Predicting Retention, Overdoses, and All-Cause Mortality Among US Military Veterans Treated with Buprenorphine for Opioid Use Disorder
Adam J. Gordon
University of Utah (US)
2 shared publications
- Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder
- Development and Validation of Machine-Learning Algorithms Predicting Retention, Overdoses, and All-Cause Mortality Among US Military Veterans Treated with Buprenorphine for Opioid Use Disorder
Katherine J. Hoggatt
San Francisco VA Medical Center (US)
2 shared publications
- Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder
- Development and Validation of Machine-Learning Algorithms Predicting Retention, Overdoses, and All-Cause Mortality Among US Military Veterans Treated with Buprenorphine for Opioid Use Disorder
Teresa J. Hudson
University of Arkansas for Medical Sciences (US)
2 shared publications
- Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder
- Development and Validation of Machine-Learning Algorithms Predicting Retention, Overdoses, and All-Cause Mortality Among US Military Veterans Treated with Buprenorphine for Opioid Use Disorder
Michael A. Cucciare
University of Arkansas for Medical Sciences
2 shared publications
In database
- Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder
- Development and Validation of Machine-Learning Algorithms Predicting Retention, Overdoses, and All-Cause Mortality Among US Military Veterans Treated with Buprenorphine for Opioid Use Disorder
Md. Tahsin Amin
2 shared publications
- Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion
- Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion
Tanim Ahmmod
2 shared publications
- Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion
- Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion
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