268 related articles for article (PubMed ID: 33245284)
1. Identification of Semantically Similar Sentences in Clinical Notes: Iterative Intermediate Training Using Multi-Task Learning.
Mahajan D; Poddar A; Liang JJ; Lin YT; Prager JM; Suryanarayanan P; Raghavan P; Tsou CH
JMIR Med Inform; 2020 Nov; 8(11):e22508. PubMed ID: 33245284
[TBL] [Abstract][Full Text] [Related]
2. The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview.
Wang Y; Fu S; Shen F; Henry S; Uzuner O; Liu H
JMIR Med Inform; 2020 Nov; 8(11):e23375. PubMed ID: 33245291
[TBL] [Abstract][Full Text] [Related]
3. Using Character-Level and Entity-Level Representations to Enhance Bidirectional Encoder Representation From Transformers-Based Clinical Semantic Textual Similarity Model: ClinicalSTS Modeling Study.
Xiong Y; Chen S; Chen Q; Yan J; Tang B
JMIR Med Inform; 2020 Dec; 8(12):e23357. PubMed ID: 33372664
[TBL] [Abstract][Full Text] [Related]
4. Incorporating Domain Knowledge Into Language Models by Using Graph Convolutional Networks for Assessing Semantic Textual Similarity: Model Development and Performance Comparison.
Chang D; Lin E; Brandt C; Taylor RA
JMIR Med Inform; 2021 Nov; 9(11):e23101. PubMed ID: 34842531
[TBL] [Abstract][Full Text] [Related]
5. Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis.
Ormerod M; Martínez Del Rincón J; Devereux B
JMIR Med Inform; 2021 May; 9(5):e23099. PubMed ID: 34037527
[TBL] [Abstract][Full Text] [Related]
6. Measurement of Semantic Textual Similarity in Clinical Texts: Comparison of Transformer-Based Models.
Yang X; He X; Zhang H; Ma Y; Bian J; Wu Y
JMIR Med Inform; 2020 Nov; 8(11):e19735. PubMed ID: 33226350
[TBL] [Abstract][Full Text] [Related]
7. Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study.
Chen Q; Rankine A; Peng Y; Aghaarabi E; Lu Z
JMIR Med Inform; 2021 Dec; 9(12):e27386. PubMed ID: 34967748
[TBL] [Abstract][Full Text] [Related]
8. Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study.
Kades K; Sellner J; Koehler G; Full PM; Lai TYE; Kleesiek J; Maier-Hein KH
JMIR Med Inform; 2021 Feb; 9(2):e22795. PubMed ID: 33533728
[TBL] [Abstract][Full Text] [Related]
9. Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and Validation Study.
Oniani D; Chandrasekar P; Sivarajkumar S; Wang Y
JMIR AI; 2023 May; 2():e44293. PubMed ID: 38875537
[TBL] [Abstract][Full Text] [Related]
10. Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study.
Saraswat N; Li C; Jiang M
JMIR AI; 2023 Sep; 2():e43483. PubMed ID: 38875534
[TBL] [Abstract][Full Text] [Related]
11. Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition.
Shen F; Liu S; Fu S; Wang Y; Henry S; Uzuner O; Liu H
JMIR Med Inform; 2021 Jan; 9(1):e24008. PubMed ID: 33502329
[TBL] [Abstract][Full Text] [Related]
12. Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records.
Chen Q; Du J; Kim S; Wilbur WJ; Lu Z
BMC Med Inform Decis Mak; 2020 Apr; 20(Suppl 1):73. PubMed ID: 32349758
[TBL] [Abstract][Full Text] [Related]
13. Identifying Risk Factors Associated With Lower Back Pain in Electronic Medical Record Free Text: Deep Learning Approach Using Clinical Note Annotations.
Jaiswal A; Katz A; Nesca M; Milios E
JMIR Med Inform; 2023 Aug; 11():e45105. PubMed ID: 37584559
[TBL] [Abstract][Full Text] [Related]
14. A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.
Chiang CC; Luo M; Dumkrieger G; Trivedi S; Chen YC; Chao CJ; Schwedt TJ; Sarker A; Banerjee I
Headache; 2024 Apr; 64(4):400-409. PubMed ID: 38525734
[TBL] [Abstract][Full Text] [Related]
15. A contextual multi-task neural approach to medication and adverse events identification from clinical text.
Narayanan S; Mannam K; Achan P; Ramesh MV; Rangan PV; Rajan SP
J Biomed Inform; 2022 Jan; 125():103960. PubMed ID: 34875387
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study.
Mitra A; Rawat BPS; McManus DD; Yu H
JMIR Med Inform; 2021 Jul; 9(7):e27527. PubMed ID: 34255697
[TBL] [Abstract][Full Text] [Related]
18. A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System.
Kim Y; Heider PM; Lally IR; Meystre SM
JMIR Med Inform; 2021 Apr; 9(4):e22797. PubMed ID: 33885370
[TBL] [Abstract][Full Text] [Related]
19. RadBERT: Adapting Transformer-based Language Models to Radiology.
Yan A; McAuley J; Lu X; Du J; Chang EY; Gentili A; Hsu CN
Radiol Artif Intell; 2022 Jul; 4(4):e210258. PubMed ID: 35923376
[TBL] [Abstract][Full Text] [Related]
20. Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)-Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study.
Li F; Jin Y; Liu W; Rawat BPS; Cai P; Yu H
JMIR Med Inform; 2019 Sep; 7(3):e14830. PubMed ID: 31516126
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]