Ruoqi Liu

Short Biography

I am a Ph.D. candidate in Computer Science and Engineering at The Ohio State University (OSU). I’m fortunately working with Prof. Ping Zhang in AIMed (Artificial Intelligence in Medicine) Lab. My research lies at the intersection of deep learning and causal inference, with applications in healthcare and biomedicine. I am particularly interested in estimating treatment effects to derive evidence-based, actionable healthcare decisions using real-world patient data.

News

• [Jan 2025] Our paper “Treatment effect estimation with multiple treatments and multiple outcomes for antihypertensive drug combinations” has been accepted to Cell Reports Medicine!
• [Sep 2024] Honored to be selected as one of the Rising Stars in Data Science 2024!
• [May 2024] Our paper “CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimation” has been accepted to Patterns
• [Dec 2023] Our paper “KG-TREAT: Pre-training for treatment effect estimation by synergizing patient data with knowledge graphs” has been accepted to AAAI 2024.
• [Nov 2023] Honored to receive the Presidential Fellowship from OSU Graduate School!
• [Oct 2023] Honored to be selected as Rising Star in EECS!
• [Apr 2023] Our paper “Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis” has been accepted to Nature Machine Intelligence.

Selected Publications

*: Equal Contributions

• Teach multimodal LLMs to comprehend electrocardiographic images
  Ruoqi Liu*, Yuelin Bai*, Xiang Yue, Ping Zhang
  arXiv 2024
  [Project Page][arXiv][Code][HF Data]

• Treatment effect estimation with multiple treatments and multiple outcomes for antihypertensive drug combinations
  Ruoqi Liu, Lang Li, Ping Zhang
  Cell Reports Medicine, 2025 (Impact factor: 11.7)
  [medRxiv]

• A deep subgrouping framework for precision drug repurposing via emulating clinical trials on real-world patient data
  Seungyeon Lee, Ruoqi Liu, Feixiong Cheng, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025 (Acceptance rate: 62/285 = 22%, applied data science track)
  

• CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimation
  Ruoqi Liu, Pin-Yu Chen, Ping Zhang
  Patterns, 2024
  [Paper] [Code]
  Selected media coverage: OHIO STATE NEWS, Clinical Research News, MedicalXpress, AZoRobotics

• KG-TREAT: Pre-training for treatment effect estimation by synergizing patient data with knowledge graphs
  Ruoqi Liu, Lingfei Wu, Ping Zhang
  AAAI Conference on Artificial Intelligence (AAAI), 2024 (Acceptance rate: 2342/9862 = 23.7%, main track, oral presentation)
  [Paper][Code]

• Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis
  Ruoqi Liu, Katherine Hunold, Jeffrey Caterino, Ping Zhang
  Nature Machine Intelligence, 2023 (Impact factor: 25.898)
  [Paper] [Code]
  Selected media coverage: OHIO STATE NEWS, HealthITAnalytics, MIT Technology Review Chinese Edition. Featured on AMIA 2024 Informatics Summit Artificial Intelligence and Data Science Year-in-Review

• Deconfounding actor-critic network with policy adaptation for dynamic treatment regimes
  Changchang Yin, Ruoqi Liu, Jeffrey Caterino, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022 (Acceptance rate: 254/1695 = 15.0%, research track)
  [Paper]

• A deep learning framework for drug repurposing via emulating clinical trials on real world patient data
  Ruoqi Liu, Lai Wei, Ping Zhang
  Nature Machine Intelligence 3:68–75, 2021 (Impact factor: 25.898)
  [Paper] [Code]
  Selected media coverage: IN BRIEF of Nature Reviews Drug Discovery, Headline of OHIO STATE NEWS, Top 5% of all research ouputs scores by Altmetric

• Estimating individual treatment effects with time-varying confounders
  Ruoqi Liu, Changchang Yin, Ping Zhang
  IEEE International Conference on Data Mining (ICDM), 2020 (Acceptance rate: 91/930 = 9.8%, regular paper, oral presentation)
  [Paper] [Code]

• Identifying sepsis subphenotypes via time-aware multi-modal auto-encoder
  Changchang Yin, Ruoqi Liu, Dongdong Zhang, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020 (Acceptance rate: 216/1279 = 16.9%, research track, oral presentation)
  [Paper] [Code] [Video]

Services

• Conference reviewer: ICLR, KDD, ICDM, SDM, AMIA
• Journal reviewer: Communications Medicine, Bioinformatics, BMC Medical Informatics and Decision Making
• Journal sub reviewer: Nature Communication, Nature Computational Science, Nature Machine Intelligence, Lancet Digital Health

Powered by Jekyll and Minimal Light theme.