88 related articles for article (PubMed ID: 32570367)
1. Detection of Muscle Weakness in Medical Texts Using Natural Language Processing.
Danilov G; Shifrin M; Strunina Y; Kotik K; Tsukanova T; Pronkina T; Ishankulov T; Makashova E; Kosyrkova A; Potapov A
Stud Health Technol Inform; 2020 Jun; 270():163-167. PubMed ID: 32570367
[TBL] [Abstract][Full Text] [Related]
2. Semiautomated Approach for Muscle Weakness Detection in Clinical Texts.
Danilov G; Shifrin M; Strunina Y; Kotik K; Tsukanova T; Pronkina T; Ishankulov T; Makashova E; Kosyrkova A; Melchenko S; Zagidullin T; Potapov A
Stud Health Technol Inform; 2020 Jun; 272():55-58. PubMed ID: 32604599
[TBL] [Abstract][Full Text] [Related]
3. Inter-Rater Reliability of Unstructured Text Labeling: Artificially vs. Naturally Intelligent Approaches.
Danilov G; Kosyrkova A; Shults M; Melchenko S; Tsukanova T; Shifrin M; Potapov A
Stud Health Technol Inform; 2021 May; 281():118-122. PubMed ID: 34042717
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. 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]
7. Prediction of severe chest injury using natural language processing from the electronic health record.
Kulshrestha S; Dligach D; Joyce C; Baker MS; Gonzalez R; O'Rourke AP; Glazer JM; Stey A; Kruser JM; Churpek MM; Afshar M
Injury; 2021 Feb; 52(2):205-212. PubMed ID: 33131794
[TBL] [Abstract][Full Text] [Related]
8. Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records.
Carson NJ; Mullin B; Sanchez MJ; Lu F; Yang K; Menezes M; Cook BL
PLoS One; 2019; 14(2):e0211116. PubMed ID: 30779800
[TBL] [Abstract][Full Text] [Related]
9. Negation Detection for Clinical Text Mining in Russian.
Funkner A; Balabaeva K; Kovalchuk S
Stud Health Technol Inform; 2020 Jun; 270():342-346. PubMed ID: 32570403
[TBL] [Abstract][Full Text] [Related]
10. Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data.
Finley GP; Pakhomov SV; McEwan R; Melton GB
AMIA Annu Symp Proc; 2016; 2016():560-569. PubMed ID: 28269852
[TBL] [Abstract][Full Text] [Related]
11. An Information Extraction Algorithm for Detecting Adverse Events in Neurosurgery Using Documents Written in a Natural Rich-in-Morphology Language.
Danilov G; Shifrin M; Strunina U; Pronkina T; Potapov A
Stud Health Technol Inform; 2019 Jul; 262():194-197. PubMed ID: 31349300
[TBL] [Abstract][Full Text] [Related]
12. Automated identification of postoperative complications within an electronic medical record using natural language processing.
Murff HJ; FitzHenry F; Matheny ME; Gentry N; Kotter KL; Crimin K; Dittus RS; Rosen AK; Elkin PL; Brown SH; Speroff T
JAMA; 2011 Aug; 306(8):848-55. PubMed ID: 21862746
[TBL] [Abstract][Full Text] [Related]
13. Accuracy of using natural language processing methods for identifying healthcare-associated infections.
Tvardik N; Kergourlay I; Bittar A; Segond F; Darmoni S; Metzger MH
Int J Med Inform; 2018 Sep; 117():96-102. PubMed ID: 30032970
[TBL] [Abstract][Full Text] [Related]
14. Extraction from Medical Records.
Dudchenko A; Dudchenko P; Ganzinger M; Kopanitsa G
Stud Health Technol Inform; 2019; 261():62-67. PubMed ID: 31156092
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing.
Barbour K; Hesdorffer DC; Tian N; Yozawitz EG; McGoldrick PE; Wolf S; McDonough TL; Nelson A; Loddenkemper T; Basma N; Johnson SB; Grinspan ZM
Epilepsia; 2019 Jun; 60(6):1209-1220. PubMed ID: 31111463
[TBL] [Abstract][Full Text] [Related]
17. Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer.
Hoogendoorn M; Szolovits P; Moons LMG; Numans ME
Artif Intell Med; 2016 May; 69():53-61. PubMed ID: 27085847
[TBL] [Abstract][Full Text] [Related]
18. Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.
Rubio-López I; Costumero R; Ambit H; Gonzalo-Martín C; Menasalvas E; Rodríguez González A
Stud Health Technol Inform; 2017; 235():251-255. PubMed ID: 28423792
[TBL] [Abstract][Full Text] [Related]
19. A comparison of rule-based and machine learning approaches for classifying patient portal messages.
Cronin RM; Fabbri D; Denny JC; Rosenbloom ST; Jackson GP
Int J Med Inform; 2017 Sep; 105():110-120. PubMed ID: 28750904
[TBL] [Abstract][Full Text] [Related]
20. A weakly supervised model for the automated detection of adverse events using clinical notes.
Sanyal J; Rubin D; Banerjee I
J Biomed Inform; 2022 Feb; 126():103969. PubMed ID: 34864210
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
