120 related articles for article (PubMed ID: 37808763)
1. 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]
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. Large language models to identify social determinants of health in electronic health records.
Guevara M; Chen S; Thomas S; Chaunzwa TL; Franco I; Kann BH; Moningi S; Qian JM; Goldstein M; Harper S; Aerts HJWL; Catalano PJ; Savova GK; Mak RH; Bitterman DS
NPJ Digit Med; 2024 Jan; 7(1):6. PubMed ID: 38200151
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. A comparison of chain-of-thought reasoning strategies across datasets and models.
Hebenstreit K; Praas R; Kiesewetter LP; Samwald M
PeerJ Comput Sci; 2024; 10():e1999. PubMed ID: 38855241
[TBL] [Abstract][Full Text] [Related]
6. Using Large Language Models to Annotate Complex Cases of Social Determinants of Health in Longitudinal Clinical Records.
Ralevski A; Taiyab N; Nossal M; Mico L; Piekos SN; Hadlock J
medRxiv; 2024 Apr; ():. PubMed ID: 38712224
[TBL] [Abstract][Full Text] [Related]
7. An evaluation of GPT models for phenotype concept recognition.
Groza T; Caufield H; Gration D; Baynam G; Haendel MA; Robinson PN; Mungall CJ; Reese JT
BMC Med Inform Decis Mak; 2024 Jan; 24(1):30. PubMed ID: 38297371
[TBL] [Abstract][Full Text] [Related]
8. GPT-4 Turbo with Vision fails to outperform text-only GPT-4 Turbo in the Japan Diagnostic Radiology Board Examination.
Hirano Y; Hanaoka S; Nakao T; Miki S; Kikuchi T; Nakamura Y; Nomura Y; Yoshikawa T; Abe O
Jpn J Radiol; 2024 May; ():. PubMed ID: 38733472
[TBL] [Abstract][Full Text] [Related]
9. Evaluating the OpenAI's GPT-3.5 Turbo's performance in extracting information from scientific articles on diabetic retinopathy.
Gue CCY; Rahim NDA; Rojas-Carabali W; Agrawal R; Rk P; Abisheganaden J; Yip WF
Syst Rev; 2024 May; 13(1):135. PubMed ID: 38755704
[TBL] [Abstract][Full Text] [Related]
10. Revisiting Relation Extraction in the era of Large Language Models.
Wadhwa S; Amir S; Wallace BC
Proc Conf Assoc Comput Linguist Meet; 2023 Jul; 2023():15566-15589. PubMed ID: 37674787
[TBL] [Abstract][Full Text] [Related]
11. The performance of ChatGPT on orthopaedic in-service training exams: A comparative study of the GPT-3.5 turbo and GPT-4 models in orthopaedic education.
Rizzo MG; Cai N; Constantinescu D
J Orthop; 2024 Apr; 50():70-75. PubMed ID: 38173829
[TBL] [Abstract][Full Text] [Related]
12. Improving large language models for clinical named entity recognition via prompt engineering.
Hu Y; Chen Q; Du J; Peng X; Keloth VK; Zuo X; Zhou Y; Li Z; Jiang X; Lu Z; Roberts K; Xu H
J Am Med Inform Assoc; 2024 Jan; ():. PubMed ID: 38281112
[TBL] [Abstract][Full Text] [Related]
13. Utilizing Large Language Models for Enhanced Clinical Trial Matching: A Study on Automation in Patient Screening.
Beattie J; Neufeld S; Yang D; Chukwuma C; Gul A; Desai N; Jiang S; Dohopolski M
Cureus; 2024 May; 16(5):e60044. PubMed ID: 38854210
[TBL] [Abstract][Full Text] [Related]
14. A Study of Biomedical Relation Extraction Using GPT Models.
Zhang J; Wibert M; Zhou H; Peng X; Chen Q; Keloth VK; Hu Y; Zhang R; Xu H; Raja K
AMIA Jt Summits Transl Sci Proc; 2024; 2024():391-400. PubMed ID: 38827097
[TBL] [Abstract][Full Text] [Related]
15. Towards Improved Radiological Diagnostics: Investigating the Utility and Limitations of GPT-3.5 Turbo and GPT-4 with Quiz Cases.
Kikuchi T; Nakao T; Nakamura Y; Hanaoka S; Mori H; Yoshikawa T
AJNR Am J Neuroradiol; 2024 May; ():. PubMed ID: 38719605
[TBL] [Abstract][Full Text] [Related]
16. Generative Large Language Models for Detection of Speech Recognition Errors in Radiology Reports.
Schmidt RA; Seah JCY; Cao K; Lim L; Lim W; Yeung J
Radiol Artif Intell; 2024 Mar; 6(2):e230205. PubMed ID: 38265301
[TBL] [Abstract][Full Text] [Related]
17. Crisis prediction among tele-mental health patients: A large language model and expert clinician comparison.
Lee C; Mohebbi M; O'Callahaghan E; Winsberg M
JMIR Ment Health; 2024 Jun; ():. PubMed ID: 38876484
[TBL] [Abstract][Full Text] [Related]
18. Generative Pre-trained Transformer (GPT) based model with relative attention for de novo drug design.
Haroon S; C A H; A S J
Comput Biol Chem; 2023 Oct; 106():107911. PubMed ID: 37450999
[TBL] [Abstract][Full Text] [Related]
19. Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods.
Zhang Y; Wang X; Hou Z; Li J
JMIR Med Inform; 2018 Dec; 6(4):e50. PubMed ID: 30559093
[TBL] [Abstract][Full Text] [Related]
20. Large language models facilitate the generation of electronic health record phenotyping algorithms.
Yan C; Ong HH; Grabowska ME; Krantz MS; Su WC; Dickson AL; Peterson JF; Feng Q; Roden DM; Stein CM; Kerchberger VE; Malin BA; Wei WQ
J Am Med Inform Assoc; 2024 Apr; ():. PubMed ID: 38613820
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]