Hua Xu

Artificial Intelligence · J. Am. Medical Informatics Assoc. · 2010–2019

A researcher in the Science corpus: 16 papers; trusts 38 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 137 citations into it; #34,720 of 35,345 by trust here, #7,010 by citations
• Molecular Biology — 103 citations into it
• Natural Language Processing — 64 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Model Architectures — 9 citations into it

Whom Hua Xu trusts overall

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

1. Chih-Jen Lin
2. Yoshua Bengio
3. Alan R. Aronson
4. Guergana K. Savova
5. Tomas Mikolov
6. Trevor J. Hastie
7. Geoffrey E. Hinton
8. Ronan Collobert
9. Alex Krizhevsky
10. Rong-En Fan

Who trusts Hua Xu overall

1. Yuan Luo
2. Zexian Zeng
3. Shengyu Liu
4. Colin G. Walsh
5. Ozlem Uzuner
6. Pranjul Yadav
7. Peter Szolovits
8. Robert J. Carroll
9. Yu Deng
10. Buzhou Tang

Papers from Hua Xu's perspective

Ordered by how strongly the researchers Hua Xu 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• The CHEMDNER corpus of chemicals and drugs and its annotation principles (2015) — 444 citations
• Deep learning in clinical natural language processing: a methodical review (2019) — 437 citations
• Clinical Abbreviation Disambiguation Using Neural Word Embeddings (2015) — 101 citations
• Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data (2013) — 986 citations
• Enhancing Clinical Concept Extraction with Contextual Embedding (2019) — 329 citations
• Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network (2015) — 168 citations
• A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries (2011) — 295 citations
• Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs (2012) — 278 citations
• Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks (2014) — 133 citations
• CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines (2017) — 343 citations
• Finding useful data across multiple biomedical data repositories using DataMed (2017) — 95 citations
• Linking Biochemical Pathways and Networks to Adverse Drug Reactions (2014) — 15 citations
• Applying active learning to high-throughput phenotyping algorithms for electronic health records data. (2013) — 115 citations
• Portability of an algorithm to identify rheumatoid arthritis in electronic health records (2012) — 230 citations
• Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. (2011) — 99 citations
• Application of information technology: MedEx: a medication information extraction system for clinical narratives (2010) — 536 citations