Leo Anthony Celi

Artificial Intelligence · 2012–2025

A researcher in the Science corpus: 31 papers; trusts 54 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 240 citations into it; #17,398 of 35,345 by trust here, #5,317 by citations
• Statistics and Probability — 76 citations into it
• Health Informatics — 74 citations into it; #123 of 240 by trust here, #29 by citations
• Epidemiology — 73 citations into it; #94 of 2,148 by trust here, #108 by citations
• Medical & Health AI — 48 citations into it; #54 of 239 by trust here, #30 by citations
• Reinforcement Learning — 47 citations into it; #987 of 2,242 by trust here, #652 by citations
• Evaluation & Benchmarks — 42 citations into it; #1,933 of 3,147 by trust here, #593 by citations
• General Health Professions — 33 citations into it

Whom Leo Anthony Celi trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Diederik P. Kingma
3. Richard S. Sutton
4. Nan Jiang
5. Geoffrey E. Hinton
6. Tomas Mikolov
7. P. Marik
8. Radford M. Neal
9. Mohit Sewak
10. Jimmy Ba

Who trusts Leo Anthony Celi overall

1. Chao Yu
2. Alistair E. W. Johnson
3. Omer Gottesman
4. Joseph D. Futoma
5. Chaochao Lu
6. Jiming Liu
7. Michael Oberst
8. Dianbo Liu
9. M. El-Haddad,
10. Aniruddh Raghu

Papers from Leo Anthony Celi's perspective

Ordered by how strongly the researchers Leo Anthony Celi trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• MIMIC-III, a freely accessible critical care database (2016) — 8,772 citations
• Evaluating Reinforcement Learning Algorithms in Observational Health Settings (2018) — 151 citations
• The eICU Collaborative Research Database, a freely available multi-center database for critical care research (2018) — 1,838 citations
• Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach (2017) — 238 citations
• The myth of generalisability in clinical research and machine learning in health care (2020) — 366 citations
• A data-driven approach to optimized medication dosing: a focus on heparin (2014) — 65 citations
• Guidelines for reinforcement learning in healthcare (2019) — 449 citations
• The MIMIC Code Repository: enabling reproducibility in critical care research (2017) — 430 citations
• Comparing Rule-Based and Deep Learning Models for Patient Phenotyping (2017) — 45 citations
• TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods (2024) — 2,500 citations
• Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018) — 199 citations
• PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods (2025) — 435 citations
• The TRIPOD-LLM reporting guideline for studies using large language models (2025) — 370 citations
• Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study. (2024) — 430 citations
• Digital literacy as a new determinant of health: A scoping review (2023) — 346 citations
• MIMIC-IV, a freely accessible electronic health record dataset (2023) — 3,152 citations
• Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review (2022) — 442 citations
• Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022) — 332 citations
• AI recognition of patient race in medical imaging: a modelling study (2021) — 504 citations
• Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment (2021) — 5 citations