What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Information retrieval
Zhiyong Lu mostly deals with Information retrieval, Artificial intelligence, Named-entity recognition, Natural language processing and Data science.
His Information retrieval research includes themes of Text corpus and World Wide Web.
His work in the fields of Artificial intelligence, such as Deep learning, intersects with other areas such as Class.
His Named-entity recognition research incorporates elements of Annotation and Normalization.
His Biomedical text mining study, which is part of a larger body of work in Natural language processing, is frequently linked to Benchmarking, bridging the gap between disciplines.
His research in Data science intersects with topics in Precision medicine, Field and Biomedicine.
His most cited work include:
- ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases (1067 citations)
- Opportunities and obstacles for deep learning in biology and medicine. (726 citations)
- PubTator: a web-based text mining tool for assisting biocuration (359 citations)
What are the main themes of his work throughout his whole career to date?
Zhiyong Lu mainly investigates Artificial intelligence, Information retrieval, Natural language processing, Deep learning and World Wide Web.
The various areas that he examines in his Artificial intelligence study include Text mining, Machine learning, Named-entity recognition and Pattern recognition.
His Information retrieval research is multidisciplinary, relying on both Annotation and Identification.
His Natural language processing research is multidisciplinary, incorporating perspectives in Semantics, Word and Data mining.
His World Wide Web study frequently intersects with other fields, such as Data science.
His Data science research is multidisciplinary, incorporating elements of Variety and Information extraction.
He most often published in these fields:
- Artificial intelligence (41.53%)
- Information retrieval (36.29%)
- Natural language processing (22.98%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (41.53%)
- Natural language processing (22.98%)
- Deep learning (14.52%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Natural language processing, Deep learning, Information retrieval and Annotation.
His Artificial intelligence research integrates issues from Machine learning and Pattern recognition.
Zhiyong Lu has included themes like Named-entity recognition and Transformer in his Natural language processing study.
His Deep learning study integrates concerns from other disciplines, such as Semantics and Reticular pseudodrusen.
His Information retrieval research incorporates themes from Probabilistic logic and Comprehension.
His Annotation study combines topics from a wide range of disciplines, such as Training set, Feature learning and Disease Ontology.
Between 2019 and 2021, his most popular works were:
- Keep up with the latest coronavirus research. (119 citations)
- Automated abnormality classification of chest radiographs using deep convolutional neural networks (32 citations)
- LitCovid: an open database of COVID-19 literature. (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
His main research concerns Artificial intelligence, Deep learning, Natural language processing, The Internet and Semantics.
The study incorporates disciplines such as Context, Ophthalmology, Confidence interval, Gold standard and Reticular pseudodrusen in addition to Artificial intelligence.
His Deep learning research includes elements of Fundus, Domain knowledge and Data science.
His Sentence study in the realm of Natural language processing connects with subjects such as Complement.
The Internet is a subfield of World Wide Web that he explores.
The concepts of his World Wide Web study are interwoven with issues in Interpretability and Workflow.
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