169 related articles for article (PubMed ID: 33372632)
1. Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods.
Thangaraj PM; Kummer BR; Lorberbaum T; Elkind MSV; Tatonetti NP
BioData Min; 2020 Dec; 13(1):21. PubMed ID: 33372632
[TBL] [Abstract][Full Text] [Related]
2. Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries.
Hong N; Wen A; Stone DJ; Tsuji S; Kingsbury PR; Rasmussen LV; Pacheco JA; Adekkanattu P; Wang F; Luo Y; Pathak J; Liu H; Jiang G
J Biomed Inform; 2019 Nov; 99():103310. PubMed ID: 31622801
[TBL] [Abstract][Full Text] [Related]
3. Automated Electronic Phenotyping of Cardioembolic Stroke.
Guan W; Ko D; Khurshid S; Trisini Lipsanopoulos AT; Ashburner JM; Harrington LX; Rost NS; Atlas SJ; Singer DE; McManus DD; Anderson CD; Lubitz SA
Stroke; 2021 Jan; 52(1):181-189. PubMed ID: 33297865
[TBL] [Abstract][Full Text] [Related]
4. A phenotyping algorithm to identify acute ischemic stroke accurately from a national biobank: the Million Veteran Program.
Imran TF; Posner D; Honerlaw J; Vassy JL; Song RJ; Ho YL; Kittner SJ; Liao KP; Cai T; O'Donnell CJ; Djousse L; Gagnon DR; Gaziano JM; Wilson PW; Cho K
Clin Epidemiol; 2018; 10():1509-1521. PubMed ID: 30425582
[TBL] [Abstract][Full Text] [Related]
5. Automated feature selection of predictors in electronic medical records data.
Gronsbell J; Minnier J; Yu S; Liao K; Cai T
Biometrics; 2019 Mar; 75(1):268-277. PubMed ID: 30353541
[TBL] [Abstract][Full Text] [Related]
6. Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.
Zhao Y; Fu S; Bielinski SJ; Decker PA; Chamberlain AM; Roger VL; Liu H; Larson NB
J Med Internet Res; 2021 Mar; 23(3):e22951. PubMed ID: 33683212
[TBL] [Abstract][Full Text] [Related]
7. Relational machine learning for electronic health record-driven phenotyping.
Peissig PL; Santos Costa V; Caldwell MD; Rottscheit C; Berg RL; Mendonca EA; Page D
J Biomed Inform; 2014 Dec; 52():260-70. PubMed ID: 25048351
[TBL] [Abstract][Full Text] [Related]
8. Weakly Semi-supervised phenotyping using Electronic Health records.
Nogues IE; Wen J; Lin Y; Liu M; Tedeschi SK; Geva A; Cai T; Hong C
J Biomed Inform; 2022 Oct; 134():104175. PubMed ID: 36064111
[TBL] [Abstract][Full Text] [Related]
9. Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study.
Maarseveen TD; Meinderink T; Reinders MJT; Knitza J; Huizinga TWJ; Kleyer A; Simon D; van den Akker EB; Knevel R
JMIR Med Inform; 2020 Nov; 8(11):e23930. PubMed ID: 33252349
[TBL] [Abstract][Full Text] [Related]
10. Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI.
Heo TS; Kim YS; Choi JM; Jeong YS; Seo SY; Lee JH; Jeon JP; Kim C
J Pers Med; 2020 Dec; 10(4):. PubMed ID: 33339385
[TBL] [Abstract][Full Text] [Related]
11. Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke.
Kim C; Zhu V; Obeid J; Lenert L
PLoS One; 2019; 14(2):e0212778. PubMed ID: 30818342
[TBL] [Abstract][Full Text] [Related]
12. High-throughput phenotyping with temporal sequences.
Estiri H; Strasser ZH; Murphy SN
J Am Med Inform Assoc; 2021 Mar; 28(4):772-781. PubMed ID: 33313899
[TBL] [Abstract][Full Text] [Related]
13. Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.
Cohen AM; Chamberlin S; Deloughery T; Nguyen M; Bedrick S; Meninger S; Ko JJ; Amin JJ; Wei AJ; Hersh W
PLoS One; 2020; 15(7):e0235574. PubMed ID: 32614911
[TBL] [Abstract][Full Text] [Related]
14. Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.
Jamian L; Wheless L; Crofford LJ; Barnado A
Arthritis Res Ther; 2019 Dec; 21(1):305. PubMed ID: 31888720
[TBL] [Abstract][Full Text] [Related]
15. Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes.
Farahani NZ; Arunachalam SP; Sundaram DSB; Pasupathy K; Enayati M; Arruda-Olson AM
Proceedings (IEEE Int Conf Bioinformatics Biomed); 2020 Dec; 2020():1932-1937. PubMed ID: 34316386
[TBL] [Abstract][Full Text] [Related]
16. Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion.
Cui J; Yang J; Zhang K; Xu G; Zhao R; Li X; Liu L; Zhu Y; Zhou L; Yu P; Xu L; Li T; Tian J; Zhao P; Yuan S; Wang Q; Guo L; Liu X
Front Neurol; 2021; 12():749599. PubMed ID: 34925213
[No Abstract] [Full Text] [Related]
17. Applying active learning to high-throughput phenotyping algorithms for electronic health records data.
Chen Y; Carroll RJ; Hinz ER; Shah A; Eyler AE; Denny JC; Xu H
J Am Med Inform Assoc; 2013 Dec; 20(e2):e253-9. PubMed ID: 23851443
[TBL] [Abstract][Full Text] [Related]
18. Optimized Identification of Advanced Chronic Kidney Disease and Absence of Kidney Disease by Combining Different Electronic Health Data Resources and by Applying Machine Learning Strategies.
Weber C; Röschke L; Modersohn L; Lohr C; Kolditz T; Hahn U; Ammon D; Betz B; Kiehntopf M
J Clin Med; 2020 Sep; 9(9):. PubMed ID: 32932685
[TBL] [Abstract][Full Text] [Related]
19. Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.
Teixeira PL; Wei WQ; Cronin RM; Mo H; VanHouten JP; Carroll RJ; LaRose E; Bastarache LA; Rosenbloom ST; Edwards TL; Roden DM; Lasko TA; Dart RA; Nikolai AM; Peissig PL; Denny JC
J Am Med Inform Assoc; 2017 Jan; 24(1):162-171. PubMed ID: 27497800
[TBL] [Abstract][Full Text] [Related]
20.
Lee HJ; Schwamm LH; Sansing L; Kamel H; de Havenon A; Turner AC; Sheth KN; Krishnaswamy S; Brandt C; Zhao H; Krumholz H; Sharma R
Res Sq; 2023 Oct; ():. PubMed ID: 37961532
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]