What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Data mining, Machine learning, Pattern recognition and Health records.
When carried out as part of a general Artificial intelligence research project, his work on Handwriting recognition and Hidden Markov model is frequently linked to work in Intelligent character recognition and Process, therefore connecting diverse disciplines of study.
His work deals with themes such as Contrast, Personalized medicine and Pattern recognition, which intersect with Data mining.
The Machine learning study combines topics in areas such as Similarity, Information retrieval, Mahalanobis distance and Drug.
His study in the field of Neural coding is also linked to topics like Stochastic gradient descent.
As part of the same scientific family, Jianying Hu usually focuses on Health records, concentrating on Data science and intersecting with Deep learning and Visual analytics.
His most cited work include:
- HMM based online handwriting recognition (263 citations)
- Risk Prediction with Electronic Health Records: A Deep Learning Approach. (193 citations)
- A machine learning based approach for table detection on the web (180 citations)
What are the main themes of his work throughout his whole career to date?
Jianying Hu mainly focuses on Artificial intelligence, Data mining, Machine learning, Pattern recognition and Drug.
Jianying Hu performs multidisciplinary studies into Artificial intelligence and Intelligent character recognition in his work.
His work on Similarity measure as part of general Data mining study is frequently linked to Set, therefore connecting diverse disciplines of science.
The study incorporates disciplines such as Document layout analysis and Word error rate in addition to Pattern recognition.
His work in Drug covers topics such as Computational biology which are related to areas like Drug reaction.
The concepts of his Handwriting recognition study are interwoven with issues in Language model and Speech recognition.
He most often published in these fields:
- Artificial intelligence (32.99%)
- Data mining (26.90%)
- Machine learning (14.21%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (32.99%)
- Drug (9.14%)
- Drug reaction (4.06%)
In recent papers he was focusing on the following fields of study:
Jianying Hu mainly investigates Artificial intelligence, Drug, Drug reaction, Machine learning and Disease.
Jianying Hu interconnects Clinical trial and Cohort in the investigation of issues within Artificial intelligence.
His work on Spontaneous reporting is typically connected to Cell specific, Laboratory test and GLYCATED HEMOGLOBIN TEST as part of general Drug study, connecting several disciplines of science.
His Drug reaction study also includes
- Computational biology and related Drug discovery,
- Adverse drug reaction, which have a strong connection to Pattern recognition, Drug candidate and Drug Pathway.
His Machine learning research integrates issues from Statistical hypothesis testing, Joint and Identification.
His Disease research incorporates elements of Observational study, Asymptomatic and Intensive care medicine.
Between 2015 and 2021, his most popular works were:
- Risk Prediction with Electronic Health Records: A Deep Learning Approach. (193 citations)
- Artificial intelligence and machine learning in clinical development: a translational perspective (86 citations)
- Artificial Intelligence for Clinical Trial Design. (62 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Machine learning, Clinical trial, Drug and Health records.
Jianying Hu does research in Artificial intelligence, focusing on Deep learning specifically.
His Machine learning research focuses on Adverse drug reaction and how it connects with Pattern recognition.
In his study, which falls under the umbrella issue of Drug, Drug repositioning and Leverage is strongly linked to Computational biology.
Within one scientific family, Jianying Hu focuses on topics pertaining to Laboratory results under Health records, and may sometimes address concerns connected to Data mining.
Jianying Hu has included themes like Convergence, Electronic health record and Linear regression in his Data mining study.
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