317 related articles for article (PubMed ID: 31943012)
1. medExtractR: A targeted, customizable approach to medication extraction from electronic health records.
Weeks HL; Beck C; McNeer E; Williams ML; Bejan CA; Denny JC; Choi L
J Am Med Inform Assoc; 2020 Mar; 27(3):407-418. PubMed ID: 31943012
[TBL] [Abstract][Full Text] [Related]
2. MedXN: an open source medication extraction and normalization tool for clinical text.
Sohn S; Clark C; Halgrim SR; Murphy SP; Chute CG; Liu H
J Am Med Inform Assoc; 2014; 21(5):858-65. PubMed ID: 24637954
[TBL] [Abstract][Full Text] [Related]
3. Building longitudinal medication dose data using medication information extracted from clinical notes in electronic health records.
McNeer E; Beck C; Weeks HL; Williams ML; James NT; Bejan CA; Choi L
J Am Med Inform Assoc; 2021 Mar; 28(4):782-790. PubMed ID: 33338223
[TBL] [Abstract][Full Text] [Related]
4. Integrating existing natural language processing tools for medication extraction from discharge summaries.
Doan S; Bastarache L; Klimkowski S; Denny JC; Xu H
J Am Med Inform Assoc; 2010; 17(5):528-31. PubMed ID: 20819857
[TBL] [Abstract][Full Text] [Related]
5. MedEx: a medication information extraction system for clinical narratives.
Xu H; Stenner SP; Doan S; Johnson KB; Waitman LR; Denny JC
J Am Med Inform Assoc; 2010; 17(1):19-24. PubMed ID: 20064797
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Comparison of ACM and CLAMP for Entity Extraction in Clinical Notes.
Shah-Mohammadi F; Cui W; Finkelstein J
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():1989-1992. PubMed ID: 34891677
[TBL] [Abstract][Full Text] [Related]
8. Linguistic approach for identification of medication names and related information in clinical narratives.
Hamon T; Grabar N
J Am Med Inform Assoc; 2010; 17(5):549-54. PubMed ID: 20819862
[TBL] [Abstract][Full Text] [Related]
9. Regular expression-based learning to extract bodyweight values from clinical notes.
Murtaugh MA; Gibson BS; Redd D; Zeng-Treitler Q
J Biomed Inform; 2015 Apr; 54():186-90. PubMed ID: 25746391
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions.
Oronoz M; Gojenola K; Pérez A; de Ilarraza AD; Casillas A
J Biomed Inform; 2015 Aug; 56():318-32. PubMed ID: 26141794
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Extracting important information from Chinese Operation Notes with natural language processing methods.
Wang H; Zhang W; Zeng Q; Li Z; Feng K; Liu L
J Biomed Inform; 2014 Apr; 48():130-6. PubMed ID: 24486562
[TBL] [Abstract][Full Text] [Related]
14. An unsupervised and customizable misspelling generator for mining noisy health-related text sources.
Sarker A; Gonzalez-Hernandez G
J Biomed Inform; 2018 Dec; 88():98-107. PubMed ID: 30445220
[TBL] [Abstract][Full Text] [Related]
15. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.
Jiang M; Chen Y; Liu M; Rosenbloom ST; Mani S; Denny JC; Xu H
J Am Med Inform Assoc; 2011; 18(5):601-6. PubMed ID: 21508414
[TBL] [Abstract][Full Text] [Related]
16. Extracting medical information from narrative patient records: the case of medication-related information.
Deléger L; Grouin C; Zweigenbaum P
J Am Med Inform Assoc; 2010; 17(5):555-8. PubMed ID: 20819863
[TBL] [Abstract][Full Text] [Related]
17. Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models.
Dandala B; Joopudi V; Tsou CH; Liang JJ; Suryanarayanan P
JMIR Med Inform; 2020 Jul; 8(7):e18417. PubMed ID: 32459650
[TBL] [Abstract][Full Text] [Related]
18. Study of effect of drug lexicons on medication extraction from electronic medical records.
Sirohi E; Peissig P
Pac Symp Biocomput; 2005; ():308-18. PubMed ID: 15759636
[TBL] [Abstract][Full Text] [Related]
19. [A customized method for information extraction from unstructured text data in the electronic medical records].
Bao XY; Huang WJ; Zhang K; Jin M; Li Y; Niu CZ
Beijing Da Xue Xue Bao Yi Xue Ban; 2018 Apr; 50(2):256-263. PubMed ID: 29643524
[TBL] [Abstract][Full Text] [Related]
20. High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge.
Patrick J; Li M
J Am Med Inform Assoc; 2010; 17(5):524-7. PubMed ID: 20819856
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
