384 related articles for article (PubMed ID: 37889530)
1. The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study.
Soliman A; Agvall B; Etminani K; Hamed O; Lingman M
J Med Internet Res; 2023 Oct; 25():e46934. PubMed ID: 37889530
[TBL] [Abstract][Full Text] [Related]
2. Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-Day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes.
Kunze KN; So MM; Padgett DE; Lyman S; MacLean CH; Fontana MA
Clin Orthop Relat Res; 2023 Sep; 481(9):1745-1759. PubMed ID: 37256278
[TBL] [Abstract][Full Text] [Related]
3. Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics.
Awan SE; Bennamoun M; Sohel F; Sanfilippo FM; Dwivedi G
ESC Heart Fail; 2019 Apr; 6(2):428-435. PubMed ID: 30810291
[TBL] [Abstract][Full Text] [Related]
4. Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction.
Wang Z; Chen X; Tan X; Yang L; Kannapur K; Vincent JL; Kessler GN; Ru B; Yang M
J Health Econ Outcomes Res; 2021; 8(2):6-13. PubMed ID: 34414250
[No Abstract] [Full Text] [Related]
5. Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms.
Lo YT; Liao JC; Chen MH; Chang CM; Li CT
BMC Med Inform Decis Mak; 2021 Oct; 21(1):288. PubMed ID: 34670553
[TBL] [Abstract][Full Text] [Related]
6. Development of a prediction model for the risk of 30-day unplanned readmission in older patients with heart failure: A multicenter retrospective study.
Zhang Y; Wang H; Yin C; Shu T; Yu J; Jian J; Jian C; Duan M; Kadier K; Xu Q; Wang X; Xiang T; Liu X
Nutr Metab Cardiovasc Dis; 2023 Oct; 33(10):1878-1887. PubMed ID: 37500347
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. 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]
10. Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.
Amritphale A; Chatterjee R; Chatterjee S; Amritphale N; Rahnavard A; Awan GM; Omar B; Fonarow GC
Adv Ther; 2021 Jun; 38(6):2954-2972. PubMed ID: 33834355
[TBL] [Abstract][Full Text] [Related]
11. Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study.
Ru B; Tan X; Liu Y; Kannapur K; Ramanan D; Kessler G; Lautsch D; Fonarow G
JMIR Form Res; 2023 Apr; 7():e41775. PubMed ID: 37067873
[TBL] [Abstract][Full Text] [Related]
12. Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.
Peng S; Huang J; Liu X; Deng J; Sun C; Tang J; Chen H; Cao W; Wang W; Duan X; Luo X; Peng S
Front Cardiovasc Med; 2022; 9():994359. PubMed ID: 36312291
[TBL] [Abstract][Full Text] [Related]
13. Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.
Li J; Liu S; Hu Y; Zhu L; Mao Y; Liu J
J Med Internet Res; 2022 Aug; 24(8):e38082. PubMed ID: 35943767
[TBL] [Abstract][Full Text] [Related]
14. Evaluating machine learning algorithms to Predict 30-day Unplanned REadmission (PURE) in Urology patients.
Welvaars K; van den Bekerom MPJ; Doornberg JN; van Haarst EP;
BMC Med Inform Decis Mak; 2023 Jun; 23(1):108. PubMed ID: 37312177
[TBL] [Abstract][Full Text] [Related]
15. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records.
Thorsen-Meyer HC; Nielsen AB; Nielsen AP; Kaas-Hansen BS; Toft P; Schierbeck J; Strøm T; Chmura PJ; Heimann M; Dybdahl L; Spangsege L; Hulsen P; Belling K; Brunak S; Perner A
Lancet Digit Health; 2020 Apr; 2(4):e179-e191. PubMed ID: 33328078
[TBL] [Abstract][Full Text] [Related]
16. Development and validation of an interpretable 3 day intensive care unit readmission prediction model using explainable boosting machines.
Hegselmann S; Ertmer C; Volkert T; Gottschalk A; Dugas M; Varghese J
Front Med (Lausanne); 2022; 9():960296. PubMed ID: 36082270
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment.
Nair M; Lundgren LE; Soliman A; Dryselius P; Fogelberg E; Petersson M; Hamed O; Triantafyllou M; Nygren J
JMIR Res Protoc; 2024 Mar; 13():e52744. PubMed ID: 38466983
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach.
Ketabi M; Andishgar A; Fereidouni Z; Sani MM; Abdollahi A; Vali M; Alkamel A; Tabrizi R
Clin Cardiol; 2024 Feb; 47(2):e24239. PubMed ID: 38402566
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
