141 related articles for article (PubMed ID: 31437922)
1. Using Ensemble Machine Learning Methods for Predicting Risk of Readmission for Heart Failure.
Mahajan SM; Ghani R
Stud Health Technol Inform; 2019 Aug; 264():243-247. PubMed ID: 31437922
[TBL] [Abstract][Full Text] [Related]
2. Combining Structured and Unstructured Data for Predicting Risk of Readmission for Heart Failure Patients.
Mahajan SM; Ghani R
Stud Health Technol Inform; 2019 Aug; 264():238-242. PubMed ID: 31437921
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Predicting Risk of 30-Day Readmissions Using Two Emerging Machine Learning Methods.
Mahajan SM; Mahajan AS; King R; Negahban S
Stud Health Technol Inform; 2018; 250():250-255. PubMed ID: 29857454
[TBL] [Abstract][Full Text] [Related]
5. A mixed-ensemble model for hospital readmission.
Turgeman L; May JH
Artif Intell Med; 2016 Sep; 72():72-82. PubMed ID: 27664509
[TBL] [Abstract][Full Text] [Related]
6. Predictive models for hospital readmission risk: A systematic review of methods.
Artetxe A; Beristain A; Graña M
Comput Methods Programs Biomed; 2018 Oct; 164():49-64. PubMed ID: 30195431
[TBL] [Abstract][Full Text] [Related]
7. Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure: Comparison of Machine Learning and Other Statistical Approaches.
Frizzell JD; Liang L; Schulte PJ; Yancy CW; Heidenreich PA; Hernandez AF; Bhatt DL; Fonarow GC; Laskey WK
JAMA Cardiol; 2017 Feb; 2(2):204-209. PubMed ID: 27784047
[TBL] [Abstract][Full Text] [Related]
8. Predicting the risk of acute care readmissions among rehabilitation inpatients: A machine learning approach.
Xue Y; Liang H; Norbury J; Gillis R; Killingworth B
J Biomed Inform; 2018 Oct; 86():143-148. PubMed ID: 30237014
[TBL] [Abstract][Full Text] [Related]
9. Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy.
Lindsay WD; Ahern CA; Tobias JS; Berlind CG; Chinniah C; Gabriel PE; Gee JC; Simone CB
Med Phys; 2019 Feb; 46(2):1054-1063. PubMed ID: 30499597
[TBL] [Abstract][Full Text] [Related]
10. Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.
Alghamdi M; Al-Mallah M; Keteyian S; Brawner C; Ehrman J; Sakr S
PLoS One; 2017; 12(7):e0179805. PubMed ID: 28738059
[TBL] [Abstract][Full Text] [Related]
11. The Promise of Machine Learning: When Will it be Delivered?
Akbilgic O; Davis RL
J Card Fail; 2019 Jun; 25(6):484-485. PubMed ID: 30978508
[TBL] [Abstract][Full Text] [Related]
12. Utilising Information of the Case Fee Catalogue to Enhance 30-Day Readmission Prediction in the German DRG System.
Eggerth A; Hayn D; Veeranki S; Stieg J; Schreier G
Stud Health Technol Inform; 2018; 255():40-44. PubMed ID: 30306903
[TBL] [Abstract][Full Text] [Related]
13. A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.
S K S; P A
J Med Syst; 2017 Nov; 41(12):201. PubMed ID: 29124453
[TBL] [Abstract][Full Text] [Related]
14. Prediction Model Using Machine Learning for Mortality in Patients with Heart Failure.
Negassa A; Ahmed S; Zolty R; Patel SR
Am J Cardiol; 2021 Aug; 153():86-93. PubMed ID: 34246419
[TBL] [Abstract][Full Text] [Related]
15. Regional Differences in Predicting Risk of 30-Day Readmissions for Heart Failure.
Mahajan SM; Mahajan AS; Negahban S
Stud Health Technol Inform; 2018; 250():245-249. PubMed ID: 29857453
[TBL] [Abstract][Full Text] [Related]
16. Incremental value of clinical data beyond claims data in predicting 30-day outcomes after heart failure hospitalization.
Hammill BG; Curtis LH; Fonarow GC; Heidenreich PA; Yancy CW; Peterson ED; Hernandez AF
Circ Cardiovasc Qual Outcomes; 2011 Jan; 4(1):60-7. PubMed ID: 21139093
[TBL] [Abstract][Full Text] [Related]
17. HR-BGCN : Predicting readmission for heart failure from electronic health records.
Ma H; Li D; Zhao J; Li W; Fu J; Li C
Artif Intell Med; 2024 Apr; 150():102829. PubMed ID: 38553167
[TBL] [Abstract][Full Text] [Related]
18. Validation of the Readmission Risk Score in Heart Failure Patients at a Tertiary Hospital.
Sudhakar S; Zhang W; Kuo YF; Alghrouz M; Barbajelata A; Sharma G
J Card Fail; 2015 Nov; 21(11):885-91. PubMed ID: 26209002
[TBL] [Abstract][Full Text] [Related]
19. Predicting Hospital Readmission: A Joint Ensemble-Learning Model.
Yu K; Xie X
IEEE J Biomed Health Inform; 2020 Feb; 24(2):447-456. PubMed ID: 31484143
[TBL] [Abstract][Full Text] [Related]
20. Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.
Zhu K; Lou Z; Zhou J; Ballester N; Kong N; Parikh P
Methods Inf Med; 2015; 54(6):560-7. PubMed ID: 26548400
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
