141 related articles for article (PubMed ID: 38199323)
1. Measuring Implicit Bias in ICU Notes Using Word-Embedding Neural Network Models.
Cobert J; Mills H; Lee A; Gologorskaya O; Espejo E; Jeon SY; Boscardin WJ; Heintz TA; Kennedy CJ; Ashana DC; Chapman AC; Raghunathan K; Smith AK; Lee SJ
Chest; 2024 Jun; 165(6):1481-1490. PubMed ID: 38199323
[TBL] [Abstract][Full Text] [Related]
2. Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.
Marafino BJ; Park M; Davies JM; Thombley R; Luft HS; Sing DC; Kazi DS; DeJong C; Boscardin WJ; Dean ML; Dudley RA
JAMA Netw Open; 2018 Dec; 1(8):e185097. PubMed ID: 30646310
[TBL] [Abstract][Full Text] [Related]
3. Impact of Different Approaches to Preparing Notes for Analysis With Natural Language Processing on the Performance of Prediction Models in Intensive Care.
Mahendra M; Luo Y; Mills H; Schenk G; Butte AJ; Dudley RA
Crit Care Explor; 2021 Jun; 3(6):e0450. PubMed ID: 34136824
[TBL] [Abstract][Full Text] [Related]
4. Using Natural Language Processing to Identify Stigmatizing Language in Labor and Birth Clinical Notes.
Barcelona V; Scharp D; Moen H; Davoudi A; Idnay BR; Cato K; Topaz M
Matern Child Health J; 2024 Mar; 28(3):578-586. PubMed ID: 38147277
[TBL] [Abstract][Full Text] [Related]
5. Examination of Stigmatizing Language in the Electronic Health Record.
Himmelstein G; Bates D; Zhou L
JAMA Netw Open; 2022 Jan; 5(1):e2144967. PubMed ID: 35084481
[TBL] [Abstract][Full Text] [Related]
6. Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.
Ye J; Yao L; Shen J; Janarthanam R; Luo Y
BMC Med Inform Decis Mak; 2020 Dec; 20(Suppl 11):295. PubMed ID: 33380338
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Negativity and Positivity in the ICU: Exploratory Development of Automated Sentiment Capture in the Electronic Health Record.
Kennedy CJ; Chiu C; Chapman AC; Gologorskaya O; Farhan H; Han M; Hodgson M; Lazzareschi D; Ashana D; Lee S; Smith AK; Espejo E; Boscardin J; Pirracchio R; Cobert J
Crit Care Explor; 2023 Oct; 5(10):e0960. PubMed ID: 37753238
[TBL] [Abstract][Full Text] [Related]
9. Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes.
Lin C; Hsu CJ; Lou YS; Yeh SJ; Lee CC; Su SL; Chen HC
J Med Internet Res; 2017 Nov; 19(11):e380. PubMed ID: 29109070
[TBL] [Abstract][Full Text] [Related]
10. Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models.
Zhang S; Kang T; Zhang X; Wen D; Elhadad N; Lei J
J Biomed Inform; 2016 Apr; 60():334-41. PubMed ID: 26923634
[TBL] [Abstract][Full Text] [Related]
11. Examining Linguistic Differences in Electronic Health Records for Diverse Patients With Diabetes: Natural Language Processing Analysis.
Bilotta I; Tonidandel S; Liaw WR; King E; Carvajal DN; Taylor A; Thamby J; Xiang Y; Tao C; Hansen M
JMIR Med Inform; 2024 May; 12():e50428. PubMed ID: 38787295
[TBL] [Abstract][Full Text] [Related]
12. Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting.
Le DV; Montgomery J; Kirkby KC; Scanlan J
J Biomed Inform; 2018 Oct; 86():49-58. PubMed ID: 30118855
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Comparison of 2 Natural Language Processing Methods for Identification of Bleeding Among Critically Ill Patients.
Taggart M; Chapman WW; Steinberg BA; Ruckel S; Pregenzer-Wenzler A; Du Y; Ferraro J; Bucher BT; Lloyd-Jones DM; Rondina MT; Shah RU
JAMA Netw Open; 2018 Oct; 1(6):e183451. PubMed ID: 30646240
[TBL] [Abstract][Full Text] [Related]
15. Natural language processing of admission notes to predict severe maternal morbidity during the delivery encounter.
Clapp MA; Kim E; James KE; Perlis RH; Kaimal AJ; McCoy TH
Am J Obstet Gynecol; 2022 Sep; 227(3):511.e1-511.e8. PubMed ID: 35430230
[TBL] [Abstract][Full Text] [Related]
16. Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.
Weng WH; Wagholikar KB; McCray AT; Szolovits P; Chueh HC
BMC Med Inform Decis Mak; 2017 Dec; 17(1):155. PubMed ID: 29191207
[TBL] [Abstract][Full Text] [Related]
17. Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease.
Silverman AL; Sushil M; Bhasuran B; Ludwig D; Buchanan J; Racz R; Parakala M; El-Kamary S; Ahima O; Belov A; Choi L; Billings M; Li Y; Habal N; Liu Q; Tiwari J; Butte AJ; Rudrapatna VA
Clin Pharmacol Ther; 2024 Jun; 115(6):1391-1399. PubMed ID: 38459719
[TBL] [Abstract][Full Text] [Related]
18. Comparison of Natural Language Processing of Clinical Notes With a Validated Risk-Stratification Tool to Predict Severe Maternal Morbidity.
Clapp MA; Kim E; James KE; Perlis RH; Kaimal AJ; McCoy TH; Easter SR
JAMA Netw Open; 2022 Oct; 5(10):e2234924. PubMed ID: 36197662
[TBL] [Abstract][Full Text] [Related]
19. Extracting Alcohol and Substance Abuse Status from Clinical Notes: The Added Value of Nursing Data.
Topaz M; Murga L; Bar-Bachar O; Cato K; Collins S
Stud Health Technol Inform; 2019 Aug; 264():1056-1060. PubMed ID: 31438086
[TBL] [Abstract][Full Text] [Related]
20. Identifying Diabetes in Clinical Notes in Hebrew: A Novel Text Classification Approach Based on Word Embedding.
Topaz M; Murga L; Grossman C; Daliyot D; Jacobson S; Rozendorn N; Zimlichman E; Furie N
Stud Health Technol Inform; 2019 Aug; 264():393-397. PubMed ID: 31437952
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
