167 related articles for article (PubMed ID: 37494080)
1. Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study.
Lolak S; Attia J; McKay GJ; Thakkinstian A
JMIR Cardio; 2023 Jul; 7():e47736. PubMed ID: 37494080
[TBL] [Abstract][Full Text] [Related]
2. Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.
Herrin J; Abraham NS; Yao X; Noseworthy PA; Inselman J; Shah ND; Ngufor C
JAMA Netw Open; 2021 May; 4(5):e2110703. PubMed ID: 34019087
[TBL] [Abstract][Full Text] [Related]
3. Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study.
Bisson A; Lemrini Y; El-Bouri W; Bodin A; Angoulvant D; Lip GYH; Fauchier L
Clin Res Cardiol; 2023 Jun; 112(6):815-823. PubMed ID: 36527472
[TBL] [Abstract][Full Text] [Related]
4. XGBoost, A Novel Explainable AI Technique, in the Prediction of Myocardial Infarction: A UK Biobank Cohort Study.
Moore A; Bell M
Clin Med Insights Cardiol; 2022; 16():11795468221133611. PubMed ID: 36386405
[TBL] [Abstract][Full Text] [Related]
5. An accurate and explainable ensemble learning method for carotid plaque prediction in an asymptomatic population.
Wu D; Cui G; Huang X; Chen Y; Liu G; Ren L; Li Y
Comput Methods Programs Biomed; 2022 Jun; 221():106842. PubMed ID: 35569238
[TBL] [Abstract][Full Text] [Related]
6. Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation.
Lu YT; Chao HJ; Chiang YC; Chen HY
J Med Internet Res; 2023 Feb; 25():e43734. PubMed ID: 36749620
[TBL] [Abstract][Full Text] [Related]
7. A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris.
Guldogan E; Yagin FH; Pinar A; Colak C; Kadry S; Kim J
Sci Rep; 2023 Dec; 13(1):22189. PubMed ID: 38092844
[TBL] [Abstract][Full Text] [Related]
8. Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries.
Islam SMS; Talukder A; Awal MA; Siddiqui MMU; Ahamad MM; Ahammed B; Rawal LB; Alizadehsani R; Abawajy J; Laranjo L; Chow CK; Maddison R
Front Cardiovasc Med; 2022; 9():839379. PubMed ID: 35433854
[TBL] [Abstract][Full Text] [Related]
9. Predicting post-stroke pneumonia using deep neural network approaches.
Ge Y; Wang Q; Wang L; Wu H; Peng C; Wang J; Xu Y; Xiong G; Zhang Y; Yi Y
Int J Med Inform; 2019 Dec; 132():103986. PubMed ID: 31629312
[TBL] [Abstract][Full Text] [Related]
10. Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction.
Tarabanis C; Kalampokis E; Khalil M; Alviar CL; Chinitz LA; Jankelson L
Cardiovasc Digit Health J; 2023 Aug; 4(4):126-132. PubMed ID: 37600443
[TBL] [Abstract][Full Text] [Related]
11. Explainable machine learning for chronic lymphocytic leukemia treatment prediction using only inexpensive tests.
Meiseles A; Paley D; Ziv M; Hadid Y; Rokach L; Tadmor T
Comput Biol Med; 2022 Jun; 145():105490. PubMed ID: 35405402
[TBL] [Abstract][Full Text] [Related]
12. Machine learning-based analysis of risk factors for atrial fibrillation recurrence after Cox-Maze IV procedure in patients with atrial fibrillation and chronic valvular disease: A retrospective cohort study with a control group.
Jiang Z; Song L; Liang C; Zhang H; Tan H; Sun Y; Guo R; Liu L
Front Cardiovasc Med; 2023; 10():1140670. PubMed ID: 37034340
[TBL] [Abstract][Full Text] [Related]
13. Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study.
Bae SW; Suffoletto B; Zhang T; Chung T; Ozolcer M; Islam MR; Dey AK
JMIR Form Res; 2023 May; 7():e39862. PubMed ID: 36809294
[TBL] [Abstract][Full Text] [Related]
14. Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis.
Ping Z; Huiyu S; Min L; Qingke B; Qiuyun L; Xu C
Front Neurosci; 2023; 17():1146197. PubMed ID: 36908783
[TBL] [Abstract][Full Text] [Related]
15. Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models.
Huang J; Jin W; Duan X; Liu X; Shu T; Fu L; Deng J; Chen H; Liu G; Jiang Y; Liu Z
Front Public Health; 2022; 10():1086339. PubMed ID: 36711330
[TBL] [Abstract][Full Text] [Related]
16. Risk Prediction of Diabetic Foot Amputation Using Machine Learning and Explainable Artificial Intelligence.
Oei CW; Chan YM; Zhang X; Leo KH; Yong E; Chong RC; Hong Q; Zhang L; Pan Y; Tan GWL; Mak MHW
J Diabetes Sci Technol; 2024 Jan; ():19322968241228606. PubMed ID: 38288696
[TBL] [Abstract][Full Text] [Related]
17. Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury.
Yang J; Peng H; Luo Y; Zhu T; Xie L
Front Med (Lausanne); 2023; 10():1165129. PubMed ID: 37275353
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Explainable machine learning model reveals its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation.
Ma Y; Zhang D; Xu J; Pang H; Hu M; Li J; Zhou S; Guo L; Yi F
BMC Cardiovasc Disord; 2023 Feb; 23(1):91. PubMed ID: 36803424
[TBL] [Abstract][Full Text] [Related]
20. [Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning].
Zhu M; Hu C; He Y; Qian Y; Tang S; Hu Q; Hao C
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Jul; 35(7):696-701. PubMed ID: 37545445
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]