These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

308 related articles for article (PubMed ID: 32062185)

  • 1. Feature rearrangement based deep learning system for predicting heart failure mortality.
    Wang Z; Zhu Y; Li D; Yin Y; Zhang J
    Comput Methods Programs Biomed; 2020 Jul; 191():105383. PubMed ID: 32062185
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Mortality prediction system for heart failure with orthogonal relief and dynamic radius means.
    Wang Z; Yao L; Li D; Ruan T; Liu M; Gao J
    Int J Med Inform; 2018 Jul; 115():10-17. PubMed ID: 29779711
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Representation learning for clinical time series prediction tasks in electronic health records.
    Ruan T; Lei L; Zhou Y; Zhai J; Zhang L; He P; Gao J
    BMC Med Inform Decis Mak; 2019 Dec; 19(Suppl 8):259. PubMed ID: 31842854
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.
    Huang J; Cai Y; Wu X; Huang X; Liu J; Hu D
    Comput Methods Programs Biomed; 2024 Nov; 256():108403. PubMed ID: 39236563
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Weight-based multiple empirical kernel learning with neighbor discriminant constraint for heart failure mortality prediction.
    Wang Z; Wang B; Zhou Y; Li D; Yin Y
    J Biomed Inform; 2020 Jan; 101():103340. PubMed ID: 31756495
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Treatment effect prediction with adversarial deep learning using electronic health records.
    Chu J; Dong W; Wang J; He K; Huang Z
    BMC Med Inform Decis Mak; 2020 Dec; 20(Suppl 4):139. PubMed ID: 33317502
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Applications of deep learning models in precision prediction of survival rates for heart failure patients.
    Zhang Q; Xu D
    Technol Health Care; 2024; 32(S1):329-337. PubMed ID: 38759059
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Multi-view ensemble learning with empirical kernel for heart failure mortality prediction.
    Wang Z; Chen L; Zhang J; Yin Y; Li D
    Int J Numer Method Biomed Eng; 2020 Jan; 36(1):e3273. PubMed ID: 31680466
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Representation learning in intraoperative vital signs for heart failure risk prediction.
    Chen Y; Qi B
    BMC Med Inform Decis Mak; 2019 Dec; 19(1):260. PubMed ID: 31818298
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A deep learning system for heart failure mortality prediction.
    Li D; Fu J; Zhao J; Qin J; Zhang L
    PLoS One; 2023; 18(2):e0276835. PubMed ID: 36827436
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Early prediction of epileptic seizures using a long-term recurrent convolutional network.
    Wei X; Zhou L; Zhang Z; Chen Z; Zhou Y
    J Neurosci Methods; 2019 Nov; 327():108395. PubMed ID: 31408651
    [TBL] [Abstract][Full Text] [Related]  

  • 12. LSTM Model for Prediction of Heart Failure in Big Data.
    Maragatham G; Devi S
    J Med Syst; 2019 Mar; 43(5):111. PubMed ID: 30888519
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.
    Chicco D; Jurman G
    BMC Med Inform Decis Mak; 2020 Feb; 20(1):16. PubMed ID: 32013925
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Endpoint prediction of heart failure using electronic health records.
    Chu J; Dong W; Huang Z
    J Biomed Inform; 2020 Sep; 109():103518. PubMed ID: 32721582
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Combining structured and unstructured data for predictive models: a deep learning approach.
    Zhang D; Yin C; Zeng J; Yuan X; Zhang P
    BMC Med Inform Decis Mak; 2020 Oct; 20(1):280. PubMed ID: 33121479
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction.
    Liu W; Liu T; Liu Z; Luo H; Pei H
    Environ Res; 2023 May; 224():115560. PubMed ID: 36842699
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Integrating multi-task and cost-sensitive learning for predicting mortality risk of chronic diseases in the elderly using real-world data.
    Cheng A; Zhang Y; Qian Z; Yuan X; Yao S; Ni W; Zheng Y; Zhang H; Lu Q; Zhao Z
    Int J Med Inform; 2024 Nov; 191():105567. PubMed ID: 39068894
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation.
    Du Z; Yang Y; Zheng J; Li Q; Lin D; Li Y; Fan J; Cheng W; Chen XH; Cai Y
    JMIR Med Inform; 2020 Jul; 8(7):e17257. PubMed ID: 32628616
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.