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 *

165 related articles for article (PubMed ID: 37210152)

  • 1. Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance.
    Pan X; Wang C; Yu Y; Reljin N; McManus DD; Darling CE; Chon KH; Mendelson Y; Lee K
    Artif Intell Med; 2023 Jun; 140():102548. PubMed ID: 37210152
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Detecting Heart Failure Decompensation by Measuring Transthoracic Bioimpedance in the Outpatient Setting: Rationale and Design of the SENTINEL-HF Study.
    Dovancescu S; Saczynski JS; Darling CE; Riistama J; Sert Kuniyoshi F; Meyer T; Goldberg R; McManus DD
    JMIR Res Protoc; 2015 Oct; 4(4):e121. PubMed ID: 26453479
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study.
    Reljin N; Posada-Quintero HF; Eaton-Robb C; Binici S; Ensom E; Ding E; Hayes A; Riistama J; Darling C; McManus D; Chon KH
    JMIR Med Inform; 2020 Aug; 8(8):e18715. PubMed ID: 32852277
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study.
    Kasaeyan Naeini E; Subramanian A; Calderon MD; Zheng K; Dutt N; Liljeberg P; Salantera S; Nelson AM; Rahmani AM
    J Med Internet Res; 2021 May; 23(5):e25079. PubMed ID: 34047710
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Bioimpedance-Based Heart Failure Deterioration Prediction Using a Prototype Fluid Accumulation Vest-Mobile Phone Dyad: An Observational Study.
    Darling CE; Dovancescu S; Saczynski JS; Riistama J; Sert Kuniyoshi F; Rock J; Meyer TE; McManus DD
    JMIR Cardio; 2017 Mar; 1(1):e1. PubMed ID: 31758769
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks.
    Ramesh J; Solatidehkordi Z; Aburukba R; Sagahyroon A
    Sensors (Basel); 2021 Oct; 21(21):. PubMed ID: 34770543
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients.
    Inan OT; Baran Pouyan M; Javaid AQ; Dowling S; Etemadi M; Dorier A; Heller JA; Bicen AO; Roy S; De Marco T; Klein L
    Circ Heart Fail; 2018 Jan; 11(1):e004313. PubMed ID: 29330154
    [TBL] [Abstract][Full Text] [Related]  

  • 8. LTH-ECG: Lottery Ticket Hypothesis-based Deep Learning Model Compression for Atrial Fibrillation Detection from Single Lead ECG On Wearable and Implantable Devices.
    Sahu I; Ukil A; Khandelwal S; Pal A
    Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():1655-1658. PubMed ID: 36085683
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device.
    Smisek R; Hejc J; Ronzhina M; Nemcova A; Marsanova L; Kolarova J; Smital L; Vitek M
    Physiol Meas; 2018 Sep; 39(9):094003. PubMed ID: 30102239
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Score and Correlation Coefficient-Based Feature Selection for Predicting Heart Failure Diagnosis by Using Machine Learning Algorithms.
    Senan EM; Abunadi I; Jadhav ME; Fati SM
    Comput Math Methods Med; 2021; 2021():8500314. PubMed ID: 34966445
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep Arm/Ear-ECG Image Learning for Highly Wearable Biometric Human Identification.
    Zhang Q; Zhou D
    Ann Biomed Eng; 2018 Jan; 46(1):122-134. PubMed ID: 29030801
    [TBL] [Abstract][Full Text] [Related]  

  • 12. COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm.
    Poola RG; Pl L; Y SS
    Results Eng; 2023 Jun; 18():101020. PubMed ID: 36945336
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study.
    Liu JH; Shih CY; Huang HL; Peng JK; Cheng SY; Tsai JS; Lai F
    J Med Internet Res; 2023 Aug; 25():e47366. PubMed ID: 37594793
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Model for classification of heart failure severity in patients with hypertrophic cardiomyopathy using a deep neural network algorithm with a 12-lead electrocardiogram.
    Togo S; Sugiura Y; Suzuki S; Ohno K; Akita K; Suwa K; Shibata SI; Kimura M; Maekawa Y
    Open Heart; 2023 Dec; 10(2):. PubMed ID: 38056911
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal.
    Bin Heyat MB; Akhtar F; Abbas SJ; Al-Sarem M; Alqarafi A; Stalin A; Abbasi R; Muaad AY; Lai D; Wu K
    Biosensors (Basel); 2022 Jun; 12(6):. PubMed ID: 35735574
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks.
    Çınar A; Tuncer SA
    Comput Methods Biomech Biomed Engin; 2021 Feb; 24(2):203-214. PubMed ID: 32955928
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Information fusion and multi-classifier system for miner fatigue recognition in plateau environments based on electrocardiography and electromyography signals.
    Chen S; Xu K; Yao X; Ge J; Li L; Zhu S; Li Z
    Comput Methods Programs Biomed; 2021 Nov; 211():106451. PubMed ID: 34644668
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning.
    Al Younis SM; Hadjileontiadis LJ; Khandoker AH; Stefanini C; Soulaidopoulos S; Arsenos P; Doundoulakis I; Gatzoulis KA; Tsioufis K
    PLoS One; 2024; 19(5):e0302639. PubMed ID: 38739639
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Detecting heart failure using wearables: a pilot study.
    Shah AJ; Isakadze N; Levantsevych O; Vest A; Clifford G; Nemati S
    Physiol Meas; 2020 May; 41(4):044001. PubMed ID: 32163936
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.
    Nguyen KA; Tandon P; Ghanavati S; Cheetirala SN; Timsina P; Freeman R; Reich D; Levin MA; Mazumdar M; Fayad ZA; Kia A
    JMIR Form Res; 2023 Oct; 7():e46905. PubMed ID: 37883177
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.