A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes. Rumeng, L., Abhyuday N, J., & Hong, Y. AMIA Annual Symposium Proceedings, 2017:1149–1158, April, 2018. ![A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex In this paper, we propose a novel neural network architecture for clinical text mining. We formulate this hybrid neural network model (HNN), composed of recurrent neural network and deep residual network, to jointly predict the presence and period assertion values associated with medical events in clinical texts. We evaluate the effectiveness of our model on a corpus of expert-annotated longitudinal Electronic Health Records (EHR) notes from Cancer patients. Our experiments show that HNN improves the joint assertion classification accuracy as compared to conventional baselines.
@article{rumeng_hybrid_2018,
title = {A hybrid {Neural} {Network} {Model} for {Joint} {Prediction} of {Presence} and {Period} {Assertions} of {Medical} {Events} in {Clinical} {Notes}},
volume = {2017},
issn = {1942-597X},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977733/},
abstract = {In this paper, we propose a novel neural network architecture for clinical text mining. We formulate this hybrid neural network model (HNN), composed of recurrent neural network and deep residual network, to jointly predict the presence and period assertion values associated with medical events in clinical texts. We evaluate the effectiveness of our model on a corpus of expert-annotated longitudinal Electronic Health Records (EHR) notes from Cancer patients. Our experiments show that HNN improves the joint assertion classification accuracy as compared to conventional baselines.},
urldate = {2018-10-01},
journal = {AMIA Annual Symposium Proceedings},
author = {Rumeng, Li and Abhyuday N, Jagannatha and Hong, Yu},
month = apr,
year = {2018},
pmid = {29854183},
pmcid = {PMC5977733},
pages = {1149--1158},
}
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