2036 related articles for article (PubMed ID: 27919371)
1. A machine learning-based framework to identify type 2 diabetes through electronic health records.
Zheng T; Xie W; Xu L; He X; Zhang Y; You M; Yang G; Chen Y
Int J Med Inform; 2017 Jan; 97():120-127. PubMed ID: 27919371
[TBL] [Abstract][Full Text] [Related]
2. Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.
Kagawa R; Kawazoe Y; Ida Y; Shinohara E; Tanaka K; Imai T; Ohe K
J Diabetes Sci Technol; 2017 Jul; 11(4):791-799. PubMed ID: 27932531
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study.
Anderson AE; Kerr WT; Thames A; Li T; Xiao J; Cohen MS
J Biomed Inform; 2016 Apr; 60():162-8. PubMed ID: 26707455
[TBL] [Abstract][Full Text] [Related]
5. Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021.
Ebrahim OA; Derbew G
Sci Rep; 2023 May; 13(1):7779. PubMed ID: 37179444
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks.
Choi BG; Rha SW; Kim SW; Kang JH; Park JY; Noh YK
Yonsei Med J; 2019 Feb; 60(2):191-199. PubMed ID: 30666841
[TBL] [Abstract][Full Text] [Related]
8. Validating an ontology-based algorithm to identify patients with type 2 diabetes mellitus in electronic health records.
Rahimi A; Liaw ST; Taggart J; Ray P; Yu H
Int J Med Inform; 2014 Oct; 83(10):768-78. PubMed ID: 25011429
[TBL] [Abstract][Full Text] [Related]
9. Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records.
Zapata RD; Huang S; Morris E; Wang C; Harle C; Magoc T; Mardini M; Loftus T; Modave F
PLoS One; 2023; 18(10):e0292888. PubMed ID: 37862334
[TBL] [Abstract][Full Text] [Related]
10. Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease.
Wang C; Chen X; Du L; Zhan Q; Yang T; Fang Z
Comput Methods Programs Biomed; 2020 May; 188():105267. PubMed ID: 31841787
[TBL] [Abstract][Full Text] [Related]
11. Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.
Anderson JP; Parikh JR; Shenfeld DK; Ivanov V; Marks C; Church BW; Laramie JM; Mardekian J; Piper BA; Willke RJ; Rublee DA
J Diabetes Sci Technol; 2015 Dec; 10(1):6-18. PubMed ID: 26685993
[TBL] [Abstract][Full Text] [Related]
12. Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records.
Nguyen BP; Pham HN; Tran H; Nghiem N; Nguyen QH; Do TTT; Tran CT; Simpson CR
Comput Methods Programs Biomed; 2019 Dec; 182():105055. PubMed ID: 31505379
[TBL] [Abstract][Full Text] [Related]
13. PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT.
Shameer K; Johnson KW; Yahi A; Miotto R; Li LI; Ricks D; Jebakaran J; Kovatch P; Sengupta PP; Gelijns S; Moskovitz A; Darrow B; David DL; Kasarskis A; Tatonetti NP; Pinney S; Dudley JT
Pac Symp Biocomput; 2017; 22():276-287. PubMed ID: 27896982
[TBL] [Abstract][Full Text] [Related]
14. Use of a K-nearest neighbors model to predict the development of type 2 diabetes within 2 years in an obese, hypertensive population.
Garcia-Carretero R; Vigil-Medina L; Mora-Jimenez I; Soguero-Ruiz C; Barquero-Perez O; Ramos-Lopez J
Med Biol Eng Comput; 2020 May; 58(5):991-1002. PubMed ID: 32100174
[TBL] [Abstract][Full Text] [Related]
15. Machine learning algorithm to evaluate risk factors of diabetic foot ulcers and its severity.
Nanda R; Nath A; Patel S; Mohapatra E
Med Biol Eng Comput; 2022 Aug; 60(8):2349-2357. PubMed ID: 35751828
[TBL] [Abstract][Full Text] [Related]
16. Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques.
Poly TN; Islam MM; Li YJ
Stud Health Technol Inform; 2022 Jun; 295():409-413. PubMed ID: 35773898
[TBL] [Abstract][Full Text] [Related]
17. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.
Dinh A; Miertschin S; Young A; Mohanty SD
BMC Med Inform Decis Mak; 2019 Nov; 19(1):211. PubMed ID: 31694707
[TBL] [Abstract][Full Text] [Related]
18. Type2 diabetes mellitus prediction using data mining algorithms based on the long-noncoding RNAs expression: a comparison of four data mining approaches.
Kazerouni F; Bayani A; Asadi F; Saeidi L; Parvizi N; Mansoori Z
BMC Bioinformatics; 2020 Aug; 21(1):372. PubMed ID: 32854616
[TBL] [Abstract][Full Text] [Related]
19. Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters.
Peng K; Tang Z; Dong L; Sun D
Sensors (Basel); 2021 Oct; 21(21):. PubMed ID: 34770274
[TBL] [Abstract][Full Text] [Related]
20. Using Electronic Health Records and Machine Learning to Predict Postpartum Depression.
Wang S; Pathak J; Zhang Y
Stud Health Technol Inform; 2019 Aug; 264():888-892. PubMed ID: 31438052
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]