M
Mahmudur Rahman
Researcher at Florida International University
Publications - 157
Citations - 15718
Mahmudur Rahman is an academic researcher from Florida International University. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 27, co-authored 127 publications receiving 11420 citations. Previous affiliations of Mahmudur Rahman include University of Miami & IBM.
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Unbiased Pain Assessment through Wearables and EHR Data: Multi-attribute Fairness Loss-based CNN Approach
TL;DR: In this article , a Multi-Attribute Fairness Loss (MAFL) based CNN model was proposed to account for any sensitive attributes included in the data and fairly predict patients' pain status while attempting to minimize the discrepancies between privileged and unprivileged groups.
Journal ArticleDOI
Effects of Antidepressants on COVID-19 Outcomes: Retrospective Study on Large-Scale Electronic Health Record Data
TL;DR: In this article , the authors investigated the causal effects of early antidepressant use on COVID-19 outcomes and found that common antidepressants may increase the risk of hospitalization or worse outcomes.
Journal ArticleDOI
“Pattern of Sexually Transmitted Infections: Treated in A Private Clinic at Dhaka, Bangladesh”
TL;DR: The prevention and control of STI is based on health education, appropriate diagnosis and treatment, and proper sex education about delaying sex debut and protective measures to prevent these infections with especial focus on monogamous relationship.
Proceedings ArticleDOI
Hyperautomation in Super Shop Using Machine Learning
TL;DR: In this article , a smart bot was employed to speed up responses of simple consumer queries by utilizing natural language processing in real time, and machine learning frameworks, such as XGBoost, linear regression, random forest, and hybrid models together, were used to predict future product demand.
Proceedings ArticleDOI
Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning
TL;DR: A new semi-supervised domain adaptation framework that combines a novel auto-encoder-based domain adaptation model with a simultaneous learning scheme providing stable improvements over state-of-the-art domain adaptation models is presented.