151 related articles for article (PubMed ID: 36701968)
61. A comparison of word embeddings for the biomedical natural language processing.
Wang Y; Liu S; Afzal N; Rastegar-Mojarad M; Wang L; Shen F; Kingsbury P; Liu H
J Biomed Inform; 2018 Nov; 87():12-20. PubMed ID: 30217670
[TBL] [Abstract][Full Text] [Related]
62. Automated classification of cancer morphology from Italian pathology reports using Natural Language Processing techniques: A rule-based approach.
Hammami L; Paglialonga A; Pruneri G; Torresani M; Sant M; Bono C; Caiani EG; Baili P
J Biomed Inform; 2021 Apr; 116():103712. PubMed ID: 33609761
[TBL] [Abstract][Full Text] [Related]
63. Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports.
Senders JT; Karhade AV; Cote DJ; Mehrtash A; Lamba N; DiRisio A; Muskens IS; Gormley WB; Smith TR; Broekman MLD; Arnaout O
JCO Clin Cancer Inform; 2019 Apr; 3():1-9. PubMed ID: 31002562
[TBL] [Abstract][Full Text] [Related]
64. Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition.
Li J; Zhou Y; Jiang X; Natarajan K; Pakhomov SV; Liu H; Xu H
J Am Med Inform Assoc; 2021 Sep; 28(10):2193-2201. PubMed ID: 34272955
[TBL] [Abstract][Full Text] [Related]
65. Validation of Case Finding Algorithms for Hepatocellular Cancer From Administrative Data and Electronic Health Records Using Natural Language Processing.
Sada Y; Hou J; Richardson P; El-Serag H; Davila J
Med Care; 2016 Feb; 54(2):e9-14. PubMed ID: 23929403
[TBL] [Abstract][Full Text] [Related]
66. AMMU: A survey of transformer-based biomedical pretrained language models.
Kalyan KS; Rajasekharan A; Sangeetha S
J Biomed Inform; 2022 Feb; 126():103982. PubMed ID: 34974190
[TBL] [Abstract][Full Text] [Related]
67. Improving the robustness and accuracy of biomedical language models through adversarial training.
Moradi M; Samwald M
J Biomed Inform; 2022 Aug; 132():104114. PubMed ID: 35717011
[TBL] [Abstract][Full Text] [Related]
68. Automatic classification of radiological reports for clinical care.
Gerevini AE; Lavelli A; Maffi A; Maroldi R; Minard AL; Serina I; Squassina G
Artif Intell Med; 2018 Sep; 91():72-81. PubMed ID: 29887337
[TBL] [Abstract][Full Text] [Related]
69. An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language.
Bhatia S; Alojail M; Sengan S; Dadheech P
Front Public Health; 2022; 10():926229. PubMed ID: 36033768
[TBL] [Abstract][Full Text] [Related]
70. Extracting comprehensive clinical information for breast cancer using deep learning methods.
Zhang X; Zhang Y; Zhang Q; Ren Y; Qiu T; Ma J; Sun Q
Int J Med Inform; 2019 Dec; 132():103985. PubMed ID: 31627032
[TBL] [Abstract][Full Text] [Related]
71. Classification of Oncology Treatment Responses from French Radiology Reports with Supervised Machine Learning.
Goldman JP; Mottin L; Zaghir J; Keszthelyi D; Lokaj B; Turbé H; Gobeil J; Ruch P; Ehrsam J; Lovis C
Stud Health Technol Inform; 2022 May; 294():849-853. PubMed ID: 35612224
[TBL] [Abstract][Full Text] [Related]
72. BioBERT: a pre-trained biomedical language representation model for biomedical text mining.
Lee J; Yoon W; Kim S; Kim D; Kim S; So CH; Kang J
Bioinformatics; 2020 Feb; 36(4):1234-1240. PubMed ID: 31501885
[TBL] [Abstract][Full Text] [Related]
73. Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke.
Kim C; Zhu V; Obeid J; Lenert L
PLoS One; 2019; 14(2):e0212778. PubMed ID: 30818342
[TBL] [Abstract][Full Text] [Related]
74. Automatic detection of patients with invasive fungal disease from free-text computed tomography (CT) scans.
Martinez D; Ananda-Rajah MR; Suominen H; Slavin MA; Thursky KA; Cavedon L
J Biomed Inform; 2015 Feb; 53():251-60. PubMed ID: 25460203
[TBL] [Abstract][Full Text] [Related]
75. Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing.
Han S; Zhang RF; Shi L; Richie R; Liu H; Tseng A; Quan W; Ryan N; Brent D; Tsui FR
J Biomed Inform; 2022 Mar; 127():103984. PubMed ID: 35007754
[TBL] [Abstract][Full Text] [Related]
76. Natural Language Processing of Radiology Reports in Patients With Hepatocellular Carcinoma to Predict Radiology Resource Utilization.
Brown AD; Kachura JR
J Am Coll Radiol; 2019 Jun; 16(6):840-844. PubMed ID: 30833164
[TBL] [Abstract][Full Text] [Related]
77. Synthetic data for annotation and extraction of family history information from clinical text.
Brekke PH; Rama T; Pilán I; Nytrø Ø; Øvrelid L
J Biomed Semantics; 2021 Jul; 12(1):11. PubMed ID: 34261535
[TBL] [Abstract][Full Text] [Related]
78. Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model.
Lau W; Lybarger K; Gunn ML; Yetisgen M
J Digit Imaging; 2023 Feb; 36(1):91-104. PubMed ID: 36253581
[TBL] [Abstract][Full Text] [Related]
79. Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology Reports: Development of a Computer-Aided Liver Cancer Diagnosis Framework.
Liu H; Zhang Z; Xu Y; Wang N; Huang Y; Yang Z; Jiang R; Chen H
J Med Internet Res; 2021 Jan; 23(1):e19689. PubMed ID: 33433395
[TBL] [Abstract][Full Text] [Related]
80. Localizing in-domain adaptation of transformer-based biomedical language models.
Buonocore TM; Crema C; Redolfi A; Bellazzi R; Parimbelli E
J Biomed Inform; 2023 Aug; 144():104431. PubMed ID: 37385327
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
