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Chaos (Woodbury, N.Y.)|March 2, 2015
Distance metric learning for complex networks: towards size-independent comparison of network structuresSadegh Aliakbary, Sadegh Motallebi, Sina Rashidian, et al.JMIR Public Health and Surveillance|April 12, 2022
Where Opioid Overdose Patients Live Far From Treatment: Geospatial Analysis of Underserved Populations in New York StateKayley Abell-Hart, Sina Rashidian, Dejun Teng, et al.Journal of Medical Internet Research|November 27, 2020
Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based ApproachHannah Yao, Sina Rashidian, Xinyu Dong, et al.Contemporary Clinical Trials Communications|April 9, 2026
Improving representativeness in trial recruitment: A data-driven approachPooyan Khajehpour, Jonathan Amar, Krisda H Chaiyachati, et al.JMIR Public Health and Surveillance|April 21, 2021
Association of Opioid Use Disorder With 2016 Presidential Voting Patterns: Cross-sectional Study in New York State at Census Tract LevelAnthony Xiang, Wei Hou, Sina Rashidian, et al.Journal of Pediatric Orthopedics. Part B|February 27, 2023
Epidemiologic patterns of adolescent idiopathic scoliosis detection and treatment in new york stateJustice U Achonu, Ki Oh, Joshua Shaw, et al.Journal of Biomedical Informatics|March 12, 2021
Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learningXinyu Dong, Jianyuan Deng, Wei Hou, et al.Journal of the American Medical Informatics Association : JAMIA|May 5, 2022
An objective framework for evaluating unrecognized bias in medical AI models predicting COVID-19 outcomesHossein Estiri, Zachary H Strasser, Sina Rashidian, et al.AMIA ... Annual Symposium Proceedings. AMIA Symposium|April 21, 2020
Machine Learning Based Opioid Overdose Prediction Using Electronic Health RecordsXinyu Dong, Sina Rashidian, Yu Wang, et al.Journal of the American Medical Informatics Association : JAMIA|April 30, 2021
Identifying risk of opioid use disorder for patients taking opioid medications with deep learningXinyu Dong, Jianyuan Deng, Sina Rashidian, et al.Pageof 2