768 related articles for article (PubMed ID: 27784047)
1. 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]
2. Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes.
Desai RJ; Wang SV; Vaduganathan M; Evers T; Schneeweiss S
JAMA Netw Open; 2020 Jan; 3(1):e1918962. PubMed ID: 31922560
[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. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.
Golas SB; Shibahara T; Agboola S; Otaki H; Sato J; Nakae T; Hisamitsu T; Kojima G; Felsted J; Kakarmath S; Kvedar J; Jethwani K
BMC Med Inform Decis Mak; 2018 Jun; 18(1):44. PubMed ID: 29929496
[TBL] [Abstract][Full Text] [Related]
5. Analysis of Machine Learning Techniques for Heart Failure Readmissions.
Mortazavi BJ; Downing NS; Bucholz EM; Dharmarajan K; Manhapra A; Li SX; Negahban SN; Krumholz HM
Circ Cardiovasc Qual Outcomes; 2016 Nov; 9(6):629-640. PubMed ID: 28263938
[TBL] [Abstract][Full Text] [Related]
6. Machine learning and LACE index for predicting 30-day readmissions after heart failure hospitalization in elderly patients.
Polo Friz H; Esposito V; Marano G; Primitz L; Bovio A; Delgrossi G; Bombelli M; Grignaffini G; Monza G; Boracchi P
Intern Emerg Med; 2022 Sep; 17(6):1727-1737. PubMed ID: 35661313
[TBL] [Abstract][Full Text] [Related]
7. Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions.
Lo-Ciganic WH; Huang JL; Zhang HH; Weiss JC; Wu Y; Kwoh CK; Donohue JM; Cochran G; Gordon AJ; Malone DC; Kuza CC; Gellad WF
JAMA Netw Open; 2019 Mar; 2(3):e190968. PubMed ID: 30901048
[TBL] [Abstract][Full Text] [Related]
8. Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database.
Miller PE; Pawar S; Vaccaro B; McCullough M; Rao P; Ghosh R; Warier P; Desai NR; Ahmad T
J Card Fail; 2019 Jun; 25(6):479-483. PubMed ID: 30738152
[TBL] [Abstract][Full Text] [Related]
9. Validated, electronic health record deployable prediction models for assessing patient risk of 30-day rehospitalization and mortality in older heart failure patients.
Eapen ZJ; Liang L; Fonarow GC; Heidenreich PA; Curtis LH; Peterson ED; Hernandez AF
JACC Heart Fail; 2013 Jun; 1(3):245-51. PubMed ID: 24621877
[TBL] [Abstract][Full Text] [Related]
10. Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage.
Goto T; Camargo CA; Faridi MK; Freishtat RJ; Hasegawa K
JAMA Netw Open; 2019 Jan; 2(1):e186937. PubMed ID: 30646206
[TBL] [Abstract][Full Text] [Related]
11. Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.
Matheny ME; Ricket I; Goodrich CA; Shah RU; Stabler ME; Perkins AM; Dorn C; Denton J; Bray BE; Gouripeddi R; Higgins J; Chapman WW; MacKenzie TA; Brown JR
JAMA Netw Open; 2021 Jan; 4(1):e2035782. PubMed ID: 33512518
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of Machine Learning Algorithms for Predicting Readmission After Acute Myocardial Infarction Using Routinely Collected Clinical Data.
Gupta S; Ko DT; Azizi P; Bouadjenek MR; Koh M; Chong A; Austin PC; Sanner S
Can J Cardiol; 2020 Jun; 36(6):878-885. PubMed ID: 32204950
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data.
Amarasingham R; Moore BJ; Tabak YP; Drazner MH; Clark CA; Zhang S; Reed WG; Swanson TS; Ma Y; Halm EA
Med Care; 2010 Nov; 48(11):981-8. PubMed ID: 20940649
[TBL] [Abstract][Full Text] [Related]
15. Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation.
Zhou H; Albrecht MA; Roberts PA; Porter P; Della PR
Aust Health Rev; 2021 Jun; 45(3):328-337. PubMed ID: 33840419
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Machine Learning-Driven Models to Predict Prognostic Outcomes in Patients Hospitalized With Heart Failure Using Electronic Health Records: Retrospective Study.
Lv H; Yang X; Wang B; Wang S; Du X; Tan Q; Hao Z; Liu Y; Yan J; Xia Y
J Med Internet Res; 2021 Apr; 23(4):e24996. PubMed ID: 33871375
[TBL] [Abstract][Full Text] [Related]
18. Analyzing 30-Day Readmission Rate for Heart Failure Using Different Predictive Models.
Mahajan S; Burman P; Hogarth M
Stud Health Technol Inform; 2016; 225():143-7. PubMed ID: 27332179
[TBL] [Abstract][Full Text] [Related]
19. Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database.
Huang Y; Talwar A; Lin Y; Aparasu RR
BMC Med Inform Decis Mak; 2022 Nov; 22(1):288. PubMed ID: 36352392
[TBL] [Abstract][Full Text] [Related]
20. Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death.
Awan SE; Bennamoun M; Sohel F; Sanfilippo FM; Chow BJ; Dwivedi G
PLoS One; 2019; 14(6):e0218760. PubMed ID: 31242238
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
