151 related articles for article (PubMed ID: 35308962)
1. Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python.
Eyre H; Chapman AB; Peterson KS; Shi J; Alba PR; Jones MM; Box TL; DuVall SL; Patterson OV
AMIA Annu Symp Proc; 2021; 2021():438-447. PubMed ID: 35308962
[TBL] [Abstract][Full Text] [Related]
2. Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: a performance comparison between GPT-4, GPT-3.5-turbo, Flan-T5, Llama-3-8B, and spaCy's rule-based and machine learning-based methods.
Bhattarai K; Oh IY; Sierra JM; Tang J; Payne PRO; Abrams Z; Lai AM
JAMIA Open; 2024 Oct; 7(3):ooae060. PubMed ID: 38962662
[TBL] [Abstract][Full Text] [Related]
3. Leveraging GPT-4 for Identifying Cancer Phenotypes in Electronic Health Records: A Performance Comparison between GPT-4, GPT-3.5-turbo, Flan-T5 and spaCy's Rule-based & Machine Learning-based methods.
Bhattarai K; Oh IY; Sierra JM; Tang J; Payne PRO; Abrams ZB; Lai AM
bioRxiv; 2024 Apr; ():. PubMed ID: 37808763
[TBL] [Abstract][Full Text] [Related]
4. NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines.
Meunier D; Pascarella A; Altukhov D; Jas M; Combrisson E; Lajnef T; Bertrand-Dubois D; Hadid V; Alamian G; Alves J; Barlaam F; Saive AL; Dehgan A; Jerbi K
Neuroimage; 2020 Oct; 219():117020. PubMed ID: 32522662
[TBL] [Abstract][Full Text] [Related]
5. Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology.
Canales L; Menke S; Marchesseau S; D'Agostino A; Del Rio-Bermudez C; Taberna M; Tello J
JMIR Med Inform; 2021 Jul; 9(7):e20492. PubMed ID: 34297002
[TBL] [Abstract][Full Text] [Related]
6. Trie-based rule processing for clinical NLP: A use-case study of n-trie, making the ConText algorithm more efficient and scalable.
Shi J; Hurdle JF
J Biomed Inform; 2018 Sep; 85():106-113. PubMed ID: 30092358
[TBL] [Abstract][Full Text] [Related]
7. Natural Language Processing in Dutch Free Text Radiology Reports: Challenges in a Small Language Area Staging Pulmonary Oncology.
Nobel JM; Puts S; Bakers FCH; Robben SGF; Dekker ALAJ
J Digit Imaging; 2020 Aug; 33(4):1002-1008. PubMed ID: 32076924
[TBL] [Abstract][Full Text] [Related]
8. Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches.
Topaz M; Murga L; Gaddis KM; McDonald MV; Bar-Bachar O; Goldberg Y; Bowles KH
J Biomed Inform; 2019 Feb; 90():103103. PubMed ID: 30639392
[TBL] [Abstract][Full Text] [Related]
9. Machine learning in medicine: a practical introduction to natural language processing.
Harrison CJ; Sidey-Gibbons CJ
BMC Med Res Methodol; 2021 Jul; 21(1):158. PubMed ID: 34332525
[TBL] [Abstract][Full Text] [Related]
10. Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework.
Zito T; Wilbert N; Wiskott L; Berkes P
Front Neuroinform; 2008; 2():8. PubMed ID: 19169361
[TBL] [Abstract][Full Text] [Related]
11. PDF text classification to leverage information extraction from publication reports.
Bui DD; Del Fiol G; Jonnalagadda S
J Biomed Inform; 2016 Jun; 61():141-8. PubMed ID: 27044929
[TBL] [Abstract][Full Text] [Related]
12. Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing.
Inglis JM; Bacchi S; Troelnikov A; Smith W; Shakib S
Int J Med Inform; 2021 Dec; 156():104611. PubMed ID: 34653809
[TBL] [Abstract][Full Text] [Related]
13. Development of machine learning and natural language processing algorithms for preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery.
Karhade AV; Bongers MER; Groot OQ; Cha TD; Doorly TP; Fogel HA; Hershman SH; Tobert DG; Srivastava SD; Bono CM; Kang JD; Harris MB; Schwab JH
Spine J; 2021 Oct; 21(10):1635-1642. PubMed ID: 32294557
[TBL] [Abstract][Full Text] [Related]
14. Social Reminiscence in Older Adults' Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning.
Ferrario A; Demiray B; Yordanova K; Luo M; Martin M
J Med Internet Res; 2020 Sep; 22(9):e19133. PubMed ID: 32866108
[TBL] [Abstract][Full Text] [Related]
15. Transformers-sklearn: a toolkit for medical language understanding with transformer-based models.
Yang F; Wang X; Ma H; Li J
BMC Med Inform Decis Mak; 2021 Jul; 21(Suppl 2):90. PubMed ID: 34330244
[TBL] [Abstract][Full Text] [Related]
16. Natural language processing and machine learning to enable automatic extraction and classification of patients' smoking status from electronic medical records.
Caccamisi A; Jørgensen L; Dalianis H; Rosenlund M
Ups J Med Sci; 2020 Nov; 125(4):316-324. PubMed ID: 32696698
[TBL] [Abstract][Full Text] [Related]
17. Clinical trial cohort selection based on multi-level rule-based natural language processing system.
Chen L; Gu Y; Ji X; Lou C; Sun Z; Li H; Gao Y; Huang Y
J Am Med Inform Assoc; 2019 Nov; 26(11):1218-1226. PubMed ID: 31300825
[TBL] [Abstract][Full Text] [Related]
18. Hybrid Ensemble-Rule Algorithm for Improved MEDLINE® Sentence Boundary Detection.
Le DX; Mork JG; Antani S
AMIA Annu Symp Proc; 2021; 2021():677-686. PubMed ID: 35308957
[TBL] [Abstract][Full Text] [Related]
19. A machine learning based approach to identify protected health information in Chinese clinical text.
Du L; Xia C; Deng Z; Lu G; Xia S; Ma J
Int J Med Inform; 2018 Aug; 116():24-32. PubMed ID: 29887232
[TBL] [Abstract][Full Text] [Related]
20. Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support.
Lederman A; Lederman R; Verspoor K
J Am Med Inform Assoc; 2022 Sep; 29(10):1810-1817. PubMed ID: 35848784
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
