OBJECTIVE: Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and medical research. We describe our clinical natural language processing (NLP) system to automatically extract medical concepts and relations related to ADEs and medications from clinical narratives. This work was part of the 2018 National NLP Clinical Challenges Shared Task and Workshop on Adverse Drug Events and Medication Extraction. MATERIALS AND METHODS: The authors developed a hybrid clinical NLP system that employs a knowledge-based general clinical NLP system for medical concepts extraction, and a task-specific deep learning system for relations identification using attention-based bidirectional long short-term memory networks. RESULTS: The systems were evaluated as part of the 2018 National NLP Clinical Challenges challenge, and our attention-based bidirectional long short-term memory networks based system obtained an F-measure of 0.9442 for relations identification task, ranking fifth at the challenge, and had <2% difference from the best system. Error analysis was also conducted targeting at figuring out the root causes and possible approaches for improvement. CONCLUSIONS: We demonstrate the generic approaches and the practice of connecting general purposed clinical NLP system to task-specific requirements with deep learning methods. Our results indicate that a well-designed hybrid NLP system is capable of ADE and medication-related information extraction, which can be used in real-world applications to support ADE-related researches and medical decisions.
OBJECTIVE: Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and medical research. We describe our clinical natural language processing (NLP) system to automatically extract medical concepts and relations related to ADEs and medications from clinical narratives. This work was part of the 2018 National NLP Clinical Challenges Shared Task and Workshop on Adverse Drug Events and Medication Extraction. MATERIALS AND METHODS: The authors developed a hybrid clinical NLP system that employs a knowledge-based general clinical NLP system for medical concepts extraction, and a task-specific deep learning system for relations identification using attention-based bidirectional long short-term memory networks. RESULTS: The systems were evaluated as part of the 2018 National NLP Clinical Challenges challenge, and our attention-based bidirectional long short-term memory networks based system obtained an F-measure of 0.9442 for relations identification task, ranking fifth at the challenge, and had <2% difference from the best system. Error analysis was also conducted targeting at figuring out the root causes and possible approaches for improvement. CONCLUSIONS: We demonstrate the generic approaches and the practice of connecting general purposed clinical NLP system to task-specific requirements with deep learning methods. Our results indicate that a well-designed hybrid NLP system is capable of ADE and medication-related information extraction, which can be used in real-world applications to support ADE-related researches and medical decisions.
Authors: Guergana K Savova; James J Masanz; Philip V Ogren; Jiaping Zheng; Sunghwan Sohn; Karin C Kipper-Schuler; Christopher G Chute Journal: J Am Med Inform Assoc Date: 2010 Sep-Oct Impact factor: 4.497
Authors: D W Bates; N Spell; D J Cullen; E Burdick; N Laird; L A Petersen; S D Small; B J Sweitzer; L L Leape Journal: JAMA Date: 1997 Jan 22-29 Impact factor: 56.272
Authors: John Fanikos; Jennifer L Cina; Steven Baroletti; Karen Fiumara; Lina Matta; Samuel Z Goldhaber Journal: Am J Cardiol Date: 2007-08-21 Impact factor: 2.778
Authors: Hongfang Liu; Suzette J Bielinski; Sunghwan Sohn; Sean Murphy; Kavishwar B Wagholikar; Siddhartha R Jonnalagadda; K E Ravikumar; Stephen T Wu; Iftikhar J Kullo; Christopher G Chute Journal: AMIA Jt Summits Transl Sci Proc Date: 2013-03-18