Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning. Liu, F., Weng, C., & Yu, H. In Richesson, R. L. & Andrews, J. E., editors, Clinical Research Informatics, of Health Informatics, pages 357–378. Springer International Publishing, Cham, 2019. ![Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Electronic health records (EHR) capture “real-world” disease and care processes and hence offer richer and more generalizable data for comparative effectiveness research than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research. A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical language processing and explains how traditional machine learning and the state-of-the-art deep learning techniques can be employed in the context of extracting and transforming narrative information in EHR to support clinical research.
@incollection{liu_advancing_2019,
address = {Cham},
series = {Health {Informatics}},
title = {Advancing {Clinical} {Research} {Through} {Natural} {Language} {Processing} on {Electronic} {Health} {Records}: {Traditional} {Machine} {Learning} {Meets} {Deep} {Learning}},
isbn = {978-3-319-98779-8},
shorttitle = {Advancing {Clinical} {Research} {Through} {Natural} {Language} {Processing} on {Electronic} {Health} {Records}},
url = {https://doi.org/10.1007/978-3-319-98779-8_17},
abstract = {Electronic health records (EHR) capture “real-world” disease and care processes and hence offer richer and more generalizable data for comparative effectiveness research than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research. A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical language processing and explains how traditional machine learning and the state-of-the-art deep learning techniques can be employed in the context of extracting and transforming narrative information in EHR to support clinical research.},
language = {en},
urldate = {2019-04-09},
booktitle = {Clinical {Research} {Informatics}},
publisher = {Springer International Publishing},
author = {Liu, Feifan and Weng, Chunhua and Yu, Hong},
editor = {Richesson, Rachel L. and Andrews, James E.},
year = {2019},
doi = {10.1007/978-3-319-98779-8_17},
keywords = {Biomedical natural language processing, Clinical research, Deep learning, Electronic health records, Machine learning, Rule-based approach},
pages = {357--378},
}
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