A maximum entropy approach to natural language processing
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Task-Oriented Extraction of Temporal Information: The Case of Clinical Narratives
TIME '06 Proceedings of the Thirteenth International Symposium on Temporal Representation and Reasoning
A temporal constraint structure for extracting temporal information from clinical narrative
Journal of Biomedical Informatics
Journal of Biomedical Informatics
An interval-based representation of temporal knowledge
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Section classification in clinical notes using supervised hidden markov model
Proceedings of the 1st ACM International Health Informatics Symposium
BioNLP '11 Proceedings of BioNLP 2011 Workshop
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We investigate the task of assigning medical events in clinical narratives to discrete time-bins. The time-bins are defined to capture when a medical event occurs relative to the hospital admission date in each clinical narrative. We model the problem as a sequence tagging task using Conditional Random Fields. We extract a combination of lexical, section-based and temporal features from medical events in each clinical narrative. The sequence tagging system outperforms a system that does not utilize any sequence information modeled using a Maximum Entropy classifier. We present results with both hand-tagged as well as automatically extracted features. We observe over 8% improvement in overall tagging accuracy with the inclusion of sequence information.