LabCompass

Seungyeon Lee

Medicine · The Ohio State University

OpenAlex ORCID
Early career · publishing since 2019Rising activity
Opioid Use Disorder TreatmentPlatelet Disorders and TreatmentsChronic Myeloid Leukemia TreatmentsStatistical Methods in Clinical TrialsMyeloproliferative Neoplasms: Diagnosis and Treatment

Publications

11

Citations

22

Est. group size

~1

Recurring co-author estimate

Active years

7

Publishing since 2019

Research summary
AI-generated

Seungyeon Lee's recent work focuses on applying machine learning and data science methods to healthcare problems, including modeling treatment effects, predicting clinical risk from electronic health records, and studying patterns in opioid and stimulant prescribing. This work combines methods from artificial intelligence (such as deep learning and causal inference) with real-world patient data and public health epidemiology.

Machine learning for healthcareTreatment effect estimation and clinical trial emulationElectronic health records and risk predictionOpioid and stimulant prescribing patternsFairness and robustness in medical AI

Publication output was minimal or absent for most of the past decade but shows a sharp increase in 2025 after a gap in 2024.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence
Publications per year over the last 10 years — averaging 1.8/year recently
17182019: 2 publications192021222023: 1 publication23242025: 8 publications82526
Recent publications
Publishes in
  • The Lancet Regional Health - Americas×2
  • Clinical Lymphoma Myeloma & Leukemia×2
  • Patterns×1
  • ACM Transactions on Computing for Healthcare×1
  • Korean Linguistics×1
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This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 19, 2026.

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