|Honghan Wu | 吴鸿汉
Research Fellow in Health Informatics
Department of Biostatistics and Health Informatics
King’s College London
- (27 Nov 2017) Our work of using knowledge graph techniques in predicting adverse drug reactions has published by Scientific Reports.
- (14 Sept 2017) our abstract titled SemEHR: Surfacing Semantic Data from Clinical Notes in Electronic Health Records for Tailored Care, Trial Recruitment and Clinical Research has been accepted to present as a poster at UK public health science conference and to be published by The Lancet.
- (20 Aug 2017) Proceedings of the 2nd International Workshop on Knowledge Discovery in Healthcare Data is published at http://ceur-ws.org/Vol-1891/.
- (5th July 2017) Our data harmonisation and search toolkit for EHR – CogStack is mentioned in Annual Report of the Chief Medical Officer 2016 by the UK Government.
I am working on
- KG4Core – a knowledge graph infrastructure for supporting bioinformatics research, which integrates structured background knowledge (e.g., drug bank, uniprot, sider), electronic health records (e.g., CRIS data), and Linked Data(e.g., linked life data).
- EBEncoding – efficient bitwise encoding for temporal/longitudinal (medical) data analytics – particularly on Adverse Drug Event analytics on CRIS data.
- NapEasy – automated PDF highlighting tool to support faster curation of case studies and reviews on Parkinson’s and Alzheimer’s disease.
Short Bio Dr Honghan Wu is a research fellow in Department of Biostatistics and Health Informatics of King’s College London. His current research focus is on annotating, analysing and searching large scale healthcare data by utilising text technologies and Knowledge Graph techniques. Before current post, Dr Wu got two Marie Curie Fellowships in two EU IAPP projects of K-Drive (in Aberdeen University, UK) and TEAM (in ELIKO, Estonia). He holds a PhD in computer science from the Southeast University. Dr Wu’s expertise is on dealing with huge volume and heterogeneous Semantic Web data, including combining information retrieval and data mining techniques with semantic technologies, including semantic search, summarisation and compression of RDF data, as well as query generation and query answering.