Reference

Note: Please cite the publication below in study manuscripts using CLAMP. Please inform us of your publication for our reference.

Soysal, E., Wang, J., Jiang, M., Wu, Y., Pakhomov, S., Liu, H., & Xu, H. (2017). CLAMP–a toolkit for efficiently building customized clinical natural language processing pipelines. Journal of the American Medical Informatics Association, 25(3), 331-336. Full text link

Awards & Publications

CLAMP is built on award-winning methods. Our team has participated in a number of clinical NLP challenges and top ranked for various tasks (see below). Now we have integrated such proven methods into CLAMP and make them available to the community.

Publications citing CLAMP

Rawal, S., Prakash, A., Adhya, S., Kulkarni, S., Anwar, S., Baral, C., & Devarakonda, M. (2019). Developing and Using Special-Purpose Lexicons for Cohort Selection from Clinical Notes. arXiv preprint arXiv:1902.09674. Yang, X., Bian, J., Gong, Y., Hogan, W. R., & Wu, Y. (2019). MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes. Drug safety, 1-11. Ngwenya, M., & Bankole, F. (2019, January). Mining and representing unstructured nicotine use data in a structured format for secondary use. In Proceedings of the 52nd Hawaii International Conference on System Sciences. Malty, A. M., Jain, S. K., Yang, P. C., Harvey, K., & Warner, J. L. (2018). Computerized approach to creating a systematic ontology of hematology/oncology regimens. JCO clinical cancer informatics, 2, 1-11. Chen, Q., Du, J., Kim, S., Wilbur, W. J., & Lu, Z. (2018). Combining rich features and deep learning for finding similar sentences in electronic medical records. Proceedings of the BioCreative/OHNLP Challenge. Sushil, M., Šuster, S., Luyckx, K., & Daelemans, W. (2018). Patient representation learning and interpretable evaluation using clinical notes. Journal of biomedical informatics, 84, 103-113. Van Le, D., Montgomery, J., Kirkby, K. C., & Scanlan, J. (2018). Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting. Journal of biomedical informatics, 86, 49-58. Caufield, J. H., Zhou, Y., Garlid, A. O., Setty, S. P., Liem, D. A., Cao, Q., ... & Zhang, L. (2018). A reference set of curated biomedical data and metadata from clinical case reports. Scientific data, 5, 180258. Shi, J., Zheng, M., Yao, L., & Ge, Y. (2018). Developing a healthcare dataset information resource (DIR) based on Semantic Web. BMC medical genomics, 11(5), 102. Ngwenya, M. (2018). Health systems data interoperability and implementation (Doctoral dissertation). Šuster, S., & Daelemans, W. (2018). Revisiting neural relation classification in clinical notes with external information. In Workshop on Health Text Mining and Information Analysis (LOUHI), workshop at EMNLP (pp. 22-28). Van Le, D., Montgomery, J., Kirkby, K. C., & Scanlan, J. (2018). Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting. Journal of biomedical informatics, 86, 49-58. Redman, J. S., Natarajan, Y., Hou, J. K., Wang, J., Hanif, M., Feng, H., ... & Kanwal, F. (2017). Accurate identification of fatty liver disease in data warehouse utilizing natural language processing. Digestive diseases and sciences, 62(10), 2713-2718.

Contact Us

Center for Computational Biomedicine

School of Biomedical Informatics

The University of Texas Health Science Center at Houston

7000 Fannin St, Houston, TX 77030

Research Coordinator

Anupama E. Gururaj, PhD

anupama.e.gururaj@uth.tmc.edu

713-500-3619

Technical Support

Jingqi Wang, MS - Scientific Programmer

jingqi.wang@uth.tmc.edu

713-500-3620

Follow Us


follow me on twitter follow me on facebook follow me on LinkedIn