What is EasyCIE

EasyCIE(Easy Clinical Information Extractor) is a rule-based clinical information extraction tool designed for non-NLP(natural language processing) expert users. It a GUI wrapper on top of EasyCIE, an UIMA-based command line version that allows executing on servers.

What's new in 3.0.0

1. Add Adaptable CPE descriptor runner, which allows run cpe descriptor with customized rule-based AE component with dynamically generated types, and allows update component configurations after compilation.
2. Add Adaptable AEs runner, which read AE descriptors from a directory, uses the project configuration xml to update AE configurations, dynamically generate rule-base types.

Additional developed rule sets

https://github.com/jianlins/EasyCIE_Hub

Citation

If you are using EasyCIE for your research work and plan to publish, please consider cite one of the following publications where you consider as most fit:

1. Jianlin Shi, Siru Liu, Liese C.C. Pruitt, etc. Using natural language processing to improve EHR structured databased surgical site infection surveillance. AIMA symposium 2019, Washington D.C
2. Jianlin Shi, Kensaku Kawamoto, Wendy Kohlmann, etc. Extracting disease onset from family history comments in the electronic health record using Fast Healthcare Interoperability Resources. AIMA symposium 2019, Washington D.C.
3. Jianlin Shi, Jianyin Shao, Kevin Graves, etc. A generic rule-based pipeline for patient cohort identification. AMIA Pre-Symposium n2c2 challenge workshop. 2018 San Francisco.
4. Jianlin Shi, John F. Hurdle. Trie-based rule processing for clinical NLP: A use-case study of n-trie, making the ConText algorithm more efficient and scalable. J Biomed Inform 2018. 85:106–13.
5. Jianlin Shi, Danielle L. Mowery D, Mingyuan Zhang, et al. Extracting Intrauterine Device Usage from Clinical Texts Using Natural Language Processing. In: 2017 IEEE International Conference on Healthcare Informatics (ICHI). 2017. 568–71.
6. Brian T. Bucher, Jianlin Shi, Jeffrey P Ferraro, et al. Portable Automated Surveillance of Surgical Site Infections Using Natural Language Processing: Development and Validation. (has been accepted to AMERICAN SURGICAL ASSOCIATION 140th Annual Meeting 2020, and the journal paper is currently under review)