214 related articles for article (PubMed ID: 27266594)
1. The use of machine learning for the identification of peripheral artery disease and future mortality risk.
Ross EG; Shah NH; Dalman RL; Nead KT; Cooke JP; Leeper NJ
J Vasc Surg; 2016 Nov; 64(5):1515-1522.e3. PubMed ID: 27266594
[TBL] [Abstract][Full Text] [Related]
2. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
Anderson AB; Grazal CF; Balazs GC; Potter BK; Dickens JF; Forsberg JA
Clin Orthop Relat Res; 2020 Jul; 478(7):0-1618. PubMed ID: 32282466
[TBL] [Abstract][Full Text] [Related]
3. Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.
Ross EG; Jung K; Dudley JT; Li L; Leeper NJ; Shah NH
Circ Cardiovasc Qual Outcomes; 2019 Mar; 12(3):e004741. PubMed ID: 30857412
[TBL] [Abstract][Full Text] [Related]
4. Administrative data are not sensitive for the detection of peripheral artery disease in the community.
Hong Y; Sebastianski M; Makowsky M; Tsuyuki R; McMurtry MS
Vasc Med; 2016 Aug; 21(4):331-6. PubMed ID: 27114456
[TBL] [Abstract][Full Text] [Related]
5. Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Study.
Shouval R; Labopin M; Bondi O; Mishan-Shamay H; Shimoni A; Ciceri F; Esteve J; Giebel S; Gorin NC; Schmid C; Polge E; Aljurf M; Kroger N; Craddock C; Bacigalupo A; Cornelissen JJ; Baron F; Unger R; Nagler A; Mohty M
J Clin Oncol; 2015 Oct; 33(28):3144-51. PubMed ID: 26240227
[TBL] [Abstract][Full Text] [Related]
6. Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.
Zack CJ; Senecal C; Kinar Y; Metzger Y; Bar-Sinai Y; Widmer RJ; Lennon R; Singh M; Bell MR; Lerman A; Gulati R
JACC Cardiovasc Interv; 2019 Jul; 12(14):1304-1311. PubMed ID: 31255564
[TBL] [Abstract][Full Text] [Related]
7. Novel screening metric for the identification of at-risk peripheral artery disease patients using administrative claims data.
Bali V; Yermilov I; Coutts K; Legorreta AP
Vasc Med; 2016 Feb; 21(1):33-40. PubMed ID: 26608733
[TBL] [Abstract][Full Text] [Related]
8. Use of Natural Language Processing to Improve Identification of Patients With Peripheral Artery Disease.
Weissler EH; Zhang J; Lippmann S; Rusincovitch S; Henao R; Jones WS
Circ Cardiovasc Interv; 2020 Oct; 13(10):e009447. PubMed ID: 33040585
[TBL] [Abstract][Full Text] [Related]
9. Predictive value of ankle-brachial index to all-cause mortality and cardiovascular mortality in Chinese patients with chronic kidney disease.
Wang Y; Guo X; Li J; Hu D; Zhao D; Ma H; Mou Q; Liu J; Xu Y
Vasa; 2012 May; 41(3):205-13. PubMed ID: 22565622
[TBL] [Abstract][Full Text] [Related]
10. Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy.
Wong NC; Lam C; Patterson L; Shayegan B
BJU Int; 2019 Jan; 123(1):51-57. PubMed ID: 29969172
[TBL] [Abstract][Full Text] [Related]
11. Prediction of all-cause mortality in coronary artery disease patients with atrial fibrillation based on machine learning models.
Liu X; Jiang J; Wei L; Xing W; Shang H; Liu G; Liu F
BMC Cardiovasc Disord; 2021 Oct; 21(1):499. PubMed ID: 34656086
[TBL] [Abstract][Full Text] [Related]
12. The intrinsic prognostic value of the ankle-brachial index is independent from its mode of calculation.
Le Bivic L; Magne J; Guy-Moyat B; Wojtyna H; Lacroix P; Blossier JD; Le Guyader A; Desormais I; Aboyans V
Vasc Med; 2019 Feb; 24(1):23-31. PubMed ID: 30426857
[TBL] [Abstract][Full Text] [Related]
13. An evidence-based score to detect prevalent peripheral artery disease (PAD).
Duval S; Massaro JM; Jaff MR; Boden WE; Alberts MJ; Califf RM; Eagle KA; D'Agostino RB; Pedley A; Fonarow GC; Murabito JM; Steg PG; Bhatt DL; Hirsch AT;
Vasc Med; 2012 Oct; 17(5):342-51. PubMed ID: 22711750
[TBL] [Abstract][Full Text] [Related]
14. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study.
Huang C; Murugiah K; Mahajan S; Li SX; Dhruva SS; Haimovich JS; Wang Y; Schulz WL; Testani JM; Wilson FP; Mena CI; Masoudi FA; Rumsfeld JS; Spertus JA; Mortazavi BJ; Krumholz HM
PLoS Med; 2018 Nov; 15(11):e1002703. PubMed ID: 30481186
[TBL] [Abstract][Full Text] [Related]
15. Mining peripheral arterial disease cases from narrative clinical notes using natural language processing.
Afzal N; Sohn S; Abram S; Scott CG; Chaudhry R; Liu H; Kullo IJ; Arruda-Olson AM
J Vasc Surg; 2017 Jun; 65(6):1753-1761. PubMed ID: 28189359
[TBL] [Abstract][Full Text] [Related]
16. Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study.
Jamthikar AD; Gupta D; Mantella LE; Saba L; Laird JR; Johri AM; Suri JS
Int J Cardiovasc Imaging; 2021 Apr; 37(4):1171-1187. PubMed ID: 33184741
[TBL] [Abstract][Full Text] [Related]
17. Prevalence and clinical implications of newly revealed, asymptomatic abnormal ankle-brachial index in patients with significant coronary artery disease.
Lee JY; Lee SW; Lee WS; Han S; Park YK; Kwon CH; Jang JY; Cho YR; Park GM; Ahn JM; Kim WJ; Park DW; Kang SJ; Kim YH; Lee CW; Park SW; Park SJ
JACC Cardiovasc Interv; 2013 Dec; 6(12):1303-13. PubMed ID: 24355120
[TBL] [Abstract][Full Text] [Related]
18. Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.
Taylor RA; Pare JR; Venkatesh AK; Mowafi H; Melnick ER; Fleischman W; Hall MK
Acad Emerg Med; 2016 Mar; 23(3):269-78. PubMed ID: 26679719
[TBL] [Abstract][Full Text] [Related]
19. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.
Corey KM; Kashyap S; Lorenzi E; Lagoo-Deenadayalan SA; Heller K; Whalen K; Balu S; Heflin MT; McDonald SR; Swaminathan M; Sendak M
PLoS Med; 2018 Nov; 15(11):e1002701. PubMed ID: 30481172
[TBL] [Abstract][Full Text] [Related]
20. Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement.
Hernandez-Suarez DF; Kim Y; Villablanca P; Gupta T; Wiley J; Nieves-Rodriguez BG; Rodriguez-Maldonado J; Feliu Maldonado R; da Luz Sant'Ana I; Sanina C; Cox-Alomar P; Ramakrishna H; Lopez-Candales A; O'Neill WW; Pinto DS; Latib A; Roche-Lima A
JACC Cardiovasc Interv; 2019 Jul; 12(14):1328-1338. PubMed ID: 31320027
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
