eHuatuo: Teaching Computer to Read Medical Records
eHuatuo is an eHealthcare project about Teaching Computer to Read Medical Records developed by Microsoft Research Asia. The goal of the project is to utilize the power of computers to help doctors process the increasing amount of data available in healthcare, ranging from text data, medical imaging data, to genomic data. We aim to link these disparate types of data together for new insights and discoveries.
eHuatuo: Teaching Computer to Read Medical Records /en-us/projects/ehuatuo/ehuatuo.pdf
Dictionary: /en-us/projects/ehuatuo/i2b2_dictionary.zip
Relation rule: /en-us/projects/ehuatuo/relationrules.zip
Related article: http://research.microsoft.com/en-us/news/features/ehealth-020610.aspx
Publications
- Yan Xu, Yining Wang, Tianren Liu, Junichi Tsujii, and Eric I-Chao Chang, An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge, in Journal of American Medical Informatics Association, BMJ Group, February 2013
- A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis, Springer, February 2013
- Yan Xu, Jianwen Zhang, Eric Chang, Maode Lai, and Zhuowen Tu, Contexts-Constrained Multiple Instance Learning for Histopathology Image Analysis (Oral), in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), June 2012
- Yan Xu, Junichi Tsujii, and Eric Chang, Named entity recognition of follow-up and time information in 20,000 radiology reports, in Journal of the American Medical Informatics Association, 28 May 2012
- Yan Xu, Kai Hong, Junichi Tsujii, and Eric Chang, Feature engineering combined with machine learning and rule-based methods for structured information, in Journal of the American Medical Informatics Association, 14 May 2012
- Yan Xu, Yue Wang, Jiahua Liu, Zhuowen Tu, Jian-Tao Sun, Junichi Tsujii, and Eric Chang, Suicide Note Sentiment Classification: A Supervised Approach Augmented by Web Data, in Biomedical Informatics Insights, Libertas Academica, January 2012
- Yan Xu, Jun-Yan Zhu, Eric Chang, and Zhuowen Tu, Multiple Clustered Instance Learning for Histopathology Cancer Image Classification, Segmentation and Clustering, in Computer Vision and Pattern Recognition (CVPR), 2012
- Yan Xu, Jiahua Liu, Jiajun Wu, Yue Wang, Zhuowen Tu, Jian-Tao Sun, Junichi Tsujii, and Eric Chang, A classification approach to coreference in discharge summaries: 2011 i2b2 challenge, in Journal of the American Medical Informatics Association, 2012
- Jun-Yan Zhu, Jiajun Wu, Yichen Wei, Eric Chang, and Zhuowen Tu, Unsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning, in Computer Vision and Pattern Recognition (CVPR), 2012
- Yan Xu, Jiahua Liu, Jiajun Wu, Yue Wang, and Eric Chang, EHUATUO: A Mention-pair Coreference System by Exploiting Document Intrinsic Latent Structures and World Knowledge in Discharge Summaries (Rank 1), in Proceedings of the 2011 i2b2/VA/Cincinnati Workshop on Challenges in Natural Language Processing for Clinical Data, 2011
