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Items: 1 to 20 of 101

1.

Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification.

Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, Song PS, Park J, Choi RK, Oh BH.

Korean Circ J. 2019 Jul;49(7):629-639. doi: 10.4070/kcj.2018.0446. Epub 2019 Mar 21.

2.

Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography.

Kwon JM, Kim KH, Jeon KH, Park J.

Echocardiography. 2019 Feb;36(2):213-218. doi: 10.1111/echo.14220. Epub 2018 Dec 4.

PMID:
30515886
3.

Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction.

Attia ZI, Kapa S, Yao X, Lopez-Jimenez F, Mohan TL, Pellikka PA, Carter RE, Shah ND, Friedman PA, Noseworthy PA.

J Cardiovasc Electrophysiol. 2019 May;30(5):668-674. doi: 10.1111/jce.13889. Epub 2019 Mar 10.

PMID:
30821035
4.

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

Kwon JM, Jeon KH, Lee M, Kim KH, Park J, Oh BH.

Pediatr Emerg Care. 2019 Jul 1. doi: 10.1097/PEC.0000000000001858. [Epub ahead of print]

PMID:
31268962
5.

Predicting Heart Failure With Preserved and Reduced Ejection Fraction: The International Collaboration on Heart Failure Subtypes.

Ho JE, Enserro D, Brouwers FP, Kizer JR, Shah SJ, Psaty BM, Bartz TM, Santhanakrishnan R, Lee DS, Chan C, Liu K, Blaha MJ, Hillege HL, van der Harst P, van Gilst WH, Kop WJ, Gansevoort RT, Vasan RS, Gardin JM, Levy D, Gottdiener JS, de Boer RA, Larson MG.

Circ Heart Fail. 2016 Jun;9(6). pii: e003116. doi: 10.1161/CIRCHEARTFAILURE.115.003116.

6.

Recovered heart failure with reduced ejection fraction and outcomes: a prospective study.

Lupón J, Díez-López C, de Antonio M, Domingo M, Zamora E, Moliner P, González B, Santesmases J, Troya MI, Bayés-Genís A.

Eur J Heart Fail. 2017 Dec;19(12):1615-1623. doi: 10.1002/ejhf.824. Epub 2017 Apr 6.

7.

Artificial intelligence algorithm for predicting mortality of patients with acute heart failure.

Kwon JM, Kim KH, Jeon KH, Lee SE, Lee HY, Cho HJ, Choi JO, Jeon ES, Kim MS, Kim JJ, Hwang KK, Chae SC, Baek SH, Kang SM, Choi DJ, Yoo BS, Kim KH, Park HY, Cho MC, Oh BH.

PLoS One. 2019 Jul 8;14(7):e0219302. doi: 10.1371/journal.pone.0219302. eCollection 2019.

8.

Scoring System Based on Electrocardiogram Features to Predict the Type of Heart Failure in Patients With Chronic Heart Failure.

Hendry PB, Krisdinarti L, Erika M.

Cardiol Res. 2016 Jun;7(3):110-116. doi: 10.14740/cr473w. Epub 2016 Jun 24.

9.

Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.

Rahimian F, Salimi-Khorshidi G, Payberah AH, Tran J, Ayala Solares R, Raimondi F, Nazarzadeh M, Canoy D, Rahimi K.

PLoS Med. 2018 Nov 20;15(11):e1002695. doi: 10.1371/journal.pmed.1002695. eCollection 2018 Nov.

10.

Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes.

Kwon JM, Jeon KH, Kim HM, Kim MJ, Lim S, Kim KH, Song PS, Park J, Choi RK, Oh BH.

Resuscitation. 2019 Jun;139:84-91. doi: 10.1016/j.resuscitation.2019.04.007. Epub 2019 Apr 9.

PMID:
30978378
11.

Prevalence and Prognostic Implications of Longitudinal Ejection Fraction Change in Heart Failure.

Savarese G, Vedin O, D'Amario D, Uijl A, Dahlström U, Rosano G, Lam CSP, Lund LH.

JACC Heart Fail. 2019 Apr;7(4):306-317. doi: 10.1016/j.jchf.2018.11.019. Epub 2019 Mar 6.

PMID:
30852236
12.

Sex-Based Differences in Heart Failure Across the Ejection Fraction Spectrum: Phenotyping, and Prognostic and Therapeutic Implications.

Stolfo D, Uijl A, Vedin O, Strömberg A, Faxén UL, Rosano GMC, Sinagra G, Dahlström U, Savarese G.

JACC Heart Fail. 2019 Jun;7(6):505-515. doi: 10.1016/j.jchf.2019.03.011.

PMID:
31146874
13.

Heart failure with mid-range ejection fraction in CHARM: characteristics, outcomes and effect of candesartan across the entire ejection fraction spectrum.

Lund LH, Claggett B, Liu J, Lam CS, Jhund PS, Rosano GM, Swedberg K, Yusuf S, Granger CB, Pfeffer MA, McMurray JJV, Solomon SD.

Eur J Heart Fail. 2018 Aug;20(8):1230-1239. doi: 10.1002/ejhf.1149. Epub 2018 Feb 12.

PMID:
29431256
14.

Community Screening for Nonischemic Cardiomyopathy in Asymptomatic Subjects ≥65 Years With Stage B Heart Failure.

Yang H, Wang Y, Nolan M, Negishi K, Okin PM, Marwick TH.

Am J Cardiol. 2016 Jun 15;117(12):1959-65. doi: 10.1016/j.amjcard.2016.03.045. Epub 2016 Apr 6.

PMID:
27138184
15.

Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach.

Sbrollini A, De Jongh MC, Ter Haar CC, Treskes RW, Man S, Burattini L, Swenne CA.

Biomed Eng Online. 2019 Feb 12;18(1):15. doi: 10.1186/s12938-019-0630-9.

16.

Heart Failure With Preserved, Borderline, and Reduced Ejection Fraction: 5-Year Outcomes.

Shah KS, Xu H, Matsouaka RA, Bhatt DL, Heidenreich PA, Hernandez AF, Devore AD, Yancy CW, Fonarow GC.

J Am Coll Cardiol. 2017 Nov 14;70(20):2476-2486. doi: 10.1016/j.jacc.2017.08.074. Epub 2017 Nov 12.

17.

Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan.

Kuo PJ, Wu SC, Chien PC, Rau CS, Chen YC, Hsieh HY, Hsieh CH.

BMJ Open. 2018 Jan 5;8(1):e018252. doi: 10.1136/bmjopen-2017-018252.

18.

Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.

Nanayakkara S, Fogarty S, Tremeer M, Ross K, Richards B, Bergmeir C, Xu S, Stub D, Smith K, Tacey M, Liew D, Pilcher D, Kaye DM.

PLoS Med. 2018 Nov 30;15(11):e1002709. doi: 10.1371/journal.pmed.1002709. eCollection 2018 Nov.

19.

Thromboembolisms in atrial fibrillation and heart failure patients with a preserved ejection fraction (HFpEF) compared to those with a reduced ejection fraction (HFrEF).

Sobue Y, Watanabe E, Lip GYH, Koshikawa M, Ichikawa T, Kawai M, Harada M, Inamasu J, Ozaki Y.

Heart Vessels. 2018 Apr;33(4):403-412. doi: 10.1007/s00380-017-1073-5. Epub 2017 Oct 24.

PMID:
29067492
20.

An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.

Kwon JM, Lee Y, Lee Y, Lee S, Park J.

J Am Heart Assoc. 2018 Jun 26;7(13). pii: e008678. doi: 10.1161/JAHA.118.008678.

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