Page 35 - AIH-1-1

Artificial Intelligence in Health NLP in EHR

https://doi.org/10.1186/s13104-019-4073-y 32. Goldstein A, Shahar Y, 2018, An automated knowledge-
based textual summarization system for longitudinal,
22. Hanauer DA, Barnholtz-Sloan JS, Beno MF, et al., 2020,
Electronic medical record search engine (EMERSE): An multivariate clinical data. J Biomed Inform, 61: 159–175.
information retrieval tool for supporting cancer research. https://doi.org/10.1016/j.jbi.2016.03.022
JCO Clin Cancer Inform, 4: 454–463.
33. He B, Dong B, Guan Y, 2017, Building a comprehensive
https://doi.org/10.1200/CCI.19.00134 syntactic and semantic corpus of Chinese clinical texts.
J Biomed Inform, 69: 203–217.
23. Baxter SL, Klie AR, Radha Saseendrakumar B, et al, 2020,
Text processing for detection of fungal ocular involvement https://doi.org/10.1016/j.jbi.2017.04.006
in critical care patients: Cross-sectional study. J Med Internet 34. Becker M, Kasper S, Böckmann B, et al., 2019, Natural
Res., 22: e18855.
Language Processing of German clinical colorectal cancer
https://doi.org/10.2196/18855 notes for guideline-based treatment evaluation. Int J Med
Inform, 127: 141–146.
24. Tao C, Filannino M, Uzuner O, 2017, Prescription extraction
using CRFs and word embeddings. J Biomed Inform, https://doi.org/10.1016/j.ijmedinf.2019.04.022
72: 60–66.
35. Afzal N, Sohn S, Abram S, et al., 2016, Identifying peripheral
https://doi.org/10.1016/j.jbi.2017.07.002 arterial disease cases using natural language processing of
clinical notes. IEEE EMBS Int Conf Biomed Health Inform,
25. Liu Z, Yang M, Wang X, et al., 2017, Entity recognition from
clinical texts via recurrent neural network. BMC Med Inform 2016: 126–131.
Decis Mak, 17: 67. https://doi.org/10.1109/BHI.2016.7455851
https://doi.org/10.1186/s12911-017-0468-7 36. Afzal N, Sohn S, Abram S, et al., 2017, Mining peripheral
arterial disease cases from narrative clinical notes using
26. Cai X, Dong S, Hu J, 2019, A deep learning model
incorporating part of speech and self-matching attention natural language processing. J Vasc Surg, 65: 1753–1761.
for named entity recognition of Chinese electronic medical https://doi.org/10.1016/j.jvs.2016.11.031
records. BMC Med Inform Decis Mak, 19: 65.
37. Hassanpour S, Bay G, Langlotz CP, 2017, Characterization
https://doi.org/10.1186/s12911-019-0762-7 of change and significance for clinical findings in radiology
reports through natural language processing. J Digit Imaging,
27. Thirukumaran CP, Zaman A, Rubery PT, et al., 2019,
Natural language processing for the identification of surgical 30: 314–322.
site infections in orthopaedics. J Bone Joint Surg Am, https://doi.org/10.1007/s10278-016-9931-8
101: 2167–2174.
38. Chase HS, Mitrani LR, Lu GG, et al., 2017, Early recognition
https://doi.org/10.2106/JBJS.19.00661 of multiple sclerosis using natural language processing of the
electronic health record. BMC Med Inform Decision Making,
28. Dipaola F, Gatti M, Pacetti V, et al., 2019, Artificial
intelligence algorithms and natural language processing 17: 24.
for the recognition of syncope patients on emergency https://doi.org/10.1186/s12911-017-0418-4
department medical records. J Clin Med, 8: 1677.
39. Zeng Z, Espino S, Roy A, et al., 2018, Using natural language
https://doi.org/10.3390/jcm8101677 processing and machine learning to identify breast cancer
local recurrence. BMC Bioinformatics, 19: 498.
29. Maarseveen TD, Meinderink T, Reinders MJ, et al., 2020,
Machine learning electronic health record identification https://doi.org/10.1186/s12859-018-2466-x
of patients with rheumatoid arthritis: Algorithm pipeline 40. Jones BE, South BR, Shao Y, et al., 2018, Development and
development and validation study. JMIR Med Inform, validation of a natural language processing tool to identify
8: e23930.
patients treated for pneumonia across VA emergency
https://doi.org/10.2196/23930 department. Appl Clin Inform, 9: 122–128.
30. Gregg JR, Lang M, Wang LL, et al., 2017, Automating the https://doi.org/10.1055/s-0038-1626725
determination of prostate cancer risk strata from electronic 41. Goff DJ, Loehfelm TW, 2018, Automated radiology report
medical records. JCO Clin Cancer Inform, 1: CCI.16.00045.
summarization using an open-source natural language
https://doi.org/10.1200/CCI.16.00045 processing pipeline. J Digit Imaging, 31: 185–192.
31. Nath C, Albaghdadi MS, Jonnalagadda SR, 2016, A natural https://doi.org/10.1007/s10278-017-0030-2
language processing tool for large-scale data extraction from 42. Bai T, Chanda AK, Egleston BL, et al., 2018, EHR
echocardiography reports. PLoS One, 11: e0153749.
phenotyping via jointly embedding medical concepts and
https://doi.org/10.1371/journal.pone.0153749 words into a unified vector space. BMC Med Inform Decision

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