TY - JOUR
T1 - Towards generating a patient's timeline
T2 - Extracting temporal relationships from clinical notes
AU - Nikfarjam, Azadeh
AU - Emadzadeh, Ehsan
AU - Gonzalez, Graciela
N1 - Funding Information:
The authors would like to thank the organizing committee members of the 2012 i2b2 Challenge, enabling the development of the methods presented. GG acknowledges the example cases presented in the introduction for which automatic timelines could be useful were inspired by discussions with Dr. Sarada Panchanathan, a pediatrician at Maricopa Integrated Health Systems and Clinical Faculty at ASU BMI. Proofreading by fellow lab member Rachel Ginn is greatly appreciated. The i2b2 NLP challenge was supported by Informatics for Integrating Biology and the Bedside (i2b2), grant number NIH NLM 2U54LM008748, and Challenges in Natural Language Processing for Clinical Narratives, grant number NIH NLM 1R13LM011411-01. This work was partially supported by NIH National Library of Medicine under grant number NIH NLM 1R01LM011176. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NLM or NIH.
PY - 2013/12
Y1 - 2013/12
N2 - Clinical records include both coded and free-text fields that interact to reflect complicated patient stories. The information often covers not only the present medical condition and events experienced by the patient, but also refers to relevant events in the past (such as signs, symptoms, tests or treatments). In order to automatically construct a timeline of these events, we first need to extract the temporal relations between pairs of events or time expressions presented in the clinical notes. We designed separate extraction components for different types of temporal relations, utilizing a novel hybrid system that combines machine learning with a graph-based inference mechanism to extract the temporal links. The temporal graph is a directed graph based on parse tree dependencies of the simplified sentences and frequent pattern clues. We generalized the sentences in order to discover patterns that, given the complexities of natural language, might not be directly discoverable in the original sentences. The proposed hybrid system performance reached an F-measure of 0.63, with precision at 0.76 and recall at 0.54 on the 2012 i2b2 Natural Language Processing corpus for the temporal relation (TLink) extraction task, achieving the highest precision and third highest f-measure among participating teams in the TLink track.
AB - Clinical records include both coded and free-text fields that interact to reflect complicated patient stories. The information often covers not only the present medical condition and events experienced by the patient, but also refers to relevant events in the past (such as signs, symptoms, tests or treatments). In order to automatically construct a timeline of these events, we first need to extract the temporal relations between pairs of events or time expressions presented in the clinical notes. We designed separate extraction components for different types of temporal relations, utilizing a novel hybrid system that combines machine learning with a graph-based inference mechanism to extract the temporal links. The temporal graph is a directed graph based on parse tree dependencies of the simplified sentences and frequent pattern clues. We generalized the sentences in order to discover patterns that, given the complexities of natural language, might not be directly discoverable in the original sentences. The proposed hybrid system performance reached an F-measure of 0.63, with precision at 0.76 and recall at 0.54 on the 2012 i2b2 Natural Language Processing corpus for the temporal relation (TLink) extraction task, achieving the highest precision and third highest f-measure among participating teams in the TLink track.
KW - Automatic patient timeline
KW - Clinical text mining
KW - Machine learning
KW - Natural Language Processing
KW - Temporal graph
KW - Temporal relation extraction
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U2 - 10.1016/j.jbi.2013.11.001
DO - 10.1016/j.jbi.2013.11.001
M3 - Article
C2 - 24212118
AN - SCOPUS:84882737650
SN - 1532-0464
VL - 46
SP - S40-S47
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
IS - SUPPL.
ER -