2015 - IEEE Fellow For contributions to pattern recognition in business and health analytics, and document analysis
2010 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to pattern recognition methodologies and applications and service to IAPR
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.
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.
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
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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HMM based online handwriting recognition
Jianying Hu;M.K. Brown;W. Turin.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1996)
Risk Prediction with Electronic Health Records: A Deep Learning Approach.
Yu Cheng;Fei Wang;Ping Zhang;Jianying Hu.
siam international conference on data mining (2016)
Matching and retrieval based on the vocabulary and grammar of color patterns
A. Mojsilovic;J. Kovacevic;Jianying Hu;R.J. Safranek.
IEEE Transactions on Image Processing (2000)
Retrieval and matching of color patterns based on a predetermined vocabulary and grammar
S. Kicha Ganapathy;Jianying Hu;Jelena Kovacevic;Aleksandra Mojsilovic.
A machine learning based approach for table detection on the web
Yalin Wang;Jianying Hu.
the web conference (2002)
Artificial intelligence and machine learning in clinical development: a translational perspective
Pratik Shah;Francis Kendall;Francis Kendall;Sean Khozin;Ryan Goosen.
npj Digital Medicine (2019)
Writer independent on-line handwriting recognition using an HMM approach
Jianying Hu;Sok Gek Lim;Michael K. Brown.
Pattern Recognition (2000)
Artificial Intelligence for Clinical Trial Design.
Stefan Harrer;Pratik Shah;Bhavna Antony;Jianying Hu.
Trends in Pharmacological Sciences (2019)
Supervised patient similarity measure of heterogeneous patient records
Jimeng Sun;Fei Wang;Jianying Hu;Shahram Edabollahi.
Sigkdd Explorations (2012)
Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects
Ping Zhang;Fei Wang;Jianying Hu;Robert Sorrentino.
Scientific Reports (2015)
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