Chen Lin1, Dmitriy Dligach2, Timothy A Miller2, Steven Bethard3, Guergana K Savova2. 1. Boston Children's Hospital Boston, Boston, Massachusetts, USA chen.lin@childrens.harvard.edu. 2. Boston Children's Hospital Boston, Boston, Massachusetts, USA Harvard Medical School, Harvard University, Boston, Massachusetts, USA. 3. Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Abstract
OBJECTIVE: To develop an open-source temporal relation discovery system for the clinical domain. The system is capable of automatically inferring temporal relations between events and time expressions using a multilayered modeling strategy. It can operate at different levels of granularity--from rough temporality expressed as event relations to the document creation time (DCT) to temporal containment to fine-grained classic Allen-style relations. MATERIALS AND METHODS: We evaluated our systems on 2 clinical corpora. One is a subset of the Temporal Histories of Your Medical Events (THYME) corpus, which was used in SemEval 2015 Task 6: Clinical TempEval. The other is the 2012 Informatics for Integrating Biology and the Bedside (i2b2) challenge corpus. We designed multiple supervised machine learning models to compute the DCT relation and within-sentence temporal relations. For the i2b2 data, we also developed models and rule-based methods to recognize cross-sentence temporal relations. We used the official evaluation scripts of both challenges to make our results comparable with results of other participating systems. In addition, we conducted a feature ablation study to find out the contribution of various features to the system's performance. RESULTS: Our system achieved state-of-the-art performance on the Clinical TempEval corpus and was on par with the best systems on the i2b2 2012 corpus. Particularly, on the Clinical TempEval corpus, our system established a new F1 score benchmark, statistically significant as compared to the baseline and the best participating system. CONCLUSION: Presented here is the first open-source clinical temporal relation discovery system. It was built using a multilayered temporal modeling strategy and achieved top performance in 2 major shared tasks.
OBJECTIVE: To develop an open-source temporal relation discovery system for the clinical domain. The system is capable of automatically inferring temporal relations between events and time expressions using a multilayered modeling strategy. It can operate at different levels of granularity--from rough temporality expressed as event relations to the document creation time (DCT) to temporal containment to fine-grained classic Allen-style relations. MATERIALS AND METHODS: We evaluated our systems on 2 clinical corpora. One is a subset of the Temporal Histories of Your Medical Events (THYME) corpus, which was used in SemEval 2015 Task 6: Clinical TempEval. The other is the 2012 Informatics for Integrating Biology and the Bedside (i2b2) challenge corpus. We designed multiple supervised machine learning models to compute the DCT relation and within-sentence temporal relations. For the i2b2 data, we also developed models and rule-based methods to recognize cross-sentence temporal relations. We used the official evaluation scripts of both challenges to make our results comparable with results of other participating systems. In addition, we conducted a feature ablation study to find out the contribution of various features to the system's performance. RESULTS: Our system achieved state-of-the-art performance on the Clinical TempEval corpus and was on par with the best systems on the i2b2 2012 corpus. Particularly, on the Clinical TempEval corpus, our system established a new F1 score benchmark, statistically significant as compared to the baseline and the best participating system. CONCLUSION: Presented here is the first open-source clinical temporal relation discovery system. It was built using a multilayered temporal modeling strategy and achieved top performance in 2 major shared tasks.
Authors: Guergana Savova; Steven Bethard; Will Styler; James Martin; Martha Palmer; James Masanz; Wayne Ward Journal: AMIA Annu Symp Proc Date: 2009-11-14
Authors: George Hripcsak; Nicholas D Soulakis; Li Li; Frances P Morrison; Albert M Lai; Carol Friedman; Neil S Calman; Farzad Mostashari Journal: J Am Med Inform Assoc Date: 2009-03-04 Impact factor: 4.497
Authors: Jessica Barrett; Peter Diggle; Robin Henderson; David Taylor-Robinson Journal: J R Stat Soc Series B Stat Methodol Date: 2014-04-08 Impact factor: 4.488
Authors: Dmitriy Dligach; Steven Bethard; Lee Becker; Timothy Miller; Guergana K Savova Journal: J Am Med Inform Assoc Date: 2013-10-03 Impact factor: 4.497
Authors: Guergana K Savova; Ioana Danciu; Folami Alamudun; Timothy Miller; Chen Lin; Danielle S Bitterman; Georgia Tourassi; Jeremy L Warner Journal: Cancer Res Date: 2019-08-08 Impact factor: 12.701
Authors: Hee-Jin Lee; Min Jiang; Yonghui Wu; Christian M Shaffer; John H Cleator; Eitan A Friedman; Joshua P Lewis; Dan M Roden; Josh Denny; Hua Xu Journal: AMIA Jt Summits Transl Sci Proc Date: 2017-07-26