192 related articles for article (PubMed ID: 36679622)
1. Machine Learning Models for the Automatic Detection of Exercise Thresholds in Cardiopulmonary Exercising Tests: From Regression to Generation to Explanation.
Zignoli A
Sensors (Basel); 2023 Jan; 23(2):. PubMed ID: 36679622
[TBL] [Abstract][Full Text] [Related]
2. Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests.
Zignoli A; Fornasiero A; Rota P; Muollo V; Peyré-Tartaruga LA; Low DA; Fontana FY; Besson D; Pühringer M; Ring-Dimitriou S; Mourot L
Eur J Sport Sci; 2022 Mar; 22(3):425-435. PubMed ID: 33331795
[TBL] [Abstract][Full Text] [Related]
3. Expert-level classification of ventilatory thresholds from cardiopulmonary exercising test data with recurrent neural networks.
Zignoli A; Fornasiero A; Stella F; Pellegrini B; Schena F; Biral F; Laursen PB
Eur J Sport Sci; 2019 Oct; 19(9):1221-1229. PubMed ID: 30880591
[TBL] [Abstract][Full Text] [Related]
4. Using Machine Learning to Identify Organ System Specific Limitations to Exercise via Cardiopulmonary Exercise Testing.
Portella JJ; Andonian BJ; Brown DE; Mansur J; Wales D; West VL; Kraus WE; Hammond WE
IEEE J Biomed Health Inform; 2022 Aug; 26(8):4228-4237. PubMed ID: 35353709
[TBL] [Abstract][Full Text] [Related]
5. Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data.
Brown DE; Sharma S; Jablonski JA; Weltman A
BioData Min; 2022 Aug; 15(1):16. PubMed ID: 35964102
[TBL] [Abstract][Full Text] [Related]
6. Test-retest reliability and four-week changes in cardiopulmonary fitness in stroke patients: evaluation using a robotics-assisted tilt table.
Saengsuwan J; Berger L; Schuster-Amft C; Nef T; Hunt KJ
BMC Neurol; 2016 Sep; 16(1):163. PubMed ID: 27600918
[TBL] [Abstract][Full Text] [Related]
7. [Artificial intelligence in image analysis-fundamentals and new developments].
Pouly M; Koller T; Gottfrois P; Lionetti S
Hautarzt; 2020 Sep; 71(9):660-668. PubMed ID: 32789670
[TBL] [Abstract][Full Text] [Related]
8. De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update.
Lin E; Lin CH; Lane HY
J Chem Inf Model; 2022 Feb; 62(4):761-774. PubMed ID: 35128926
[TBL] [Abstract][Full Text] [Related]
9. Using Machine Learning-Based Algorithms to Identify and Quantify Exercise Limitations in Clinical Practice: Are We There Yet?
Schwendinger F; Biehler AK; Nagy-Huber M; Knaier R; Roth V; Dumitrescu D; Meyer FJ; Hager A; Schmidt-Trucksäss A
Med Sci Sports Exerc; 2024 Feb; 56(2):159-169. PubMed ID: 37703323
[TBL] [Abstract][Full Text] [Related]
10. Sensor-based machine learning for workflow detection and as key to detect expert level in laparoscopic suturing and knot-tying.
Kowalewski KF; Garrow CR; Schmidt MW; Benner L; Müller-Stich BP; Nickel F
Surg Endosc; 2019 Nov; 33(11):3732-3740. PubMed ID: 30790048
[TBL] [Abstract][Full Text] [Related]
11. On the interpretability of machine learning-based model for predicting hypertension.
Elshawi R; Al-Mallah MH; Sakr S
BMC Med Inform Decis Mak; 2019 Jul; 19(1):146. PubMed ID: 31357998
[TBL] [Abstract][Full Text] [Related]
12. An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks.
Ruengchaijatuporn N; Chatnuntawech I; Teerapittayanon S; Sriswasdi S; Itthipuripat S; Hemrungrojn S; Bunyabukkana P; Petchlorlian A; Chunamchai S; Chotibut T; Chunharas C
Alzheimers Res Ther; 2022 Aug; 14(1):111. PubMed ID: 35945568
[TBL] [Abstract][Full Text] [Related]
13. Machine Learning Algorithms in Neuroimaging: An Overview.
Stumpo V; Kernbach JM; van Niftrik CHB; Sebök M; Fierstra J; Regli L; Serra C; Staartjes VE
Acta Neurochir Suppl; 2022; 134():125-138. PubMed ID: 34862537
[TBL] [Abstract][Full Text] [Related]
14. Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks.
Al-Emadi S; Al-Ali A; Al-Ali A
Sensors (Basel); 2021 Jul; 21(15):. PubMed ID: 34372189
[TBL] [Abstract][Full Text] [Related]
15. MIDRC CRP10 AI interface-an integrated tool for exploring, testing and visualization of AI models.
Gorre N; Carranza E; Fuhrman J; Li H; Madduri RK; Giger M; El Naqa I
Phys Med Biol; 2023 Mar; 68(7):. PubMed ID: 36716497
[No Abstract] [Full Text] [Related]
16. Transferability of artificial neural networks for clinical document classification across hospitals: A case study on abnormality detection from radiology reports.
Hassanzadeh H; Nguyen A; Karimi S; Chu K
J Biomed Inform; 2018 Sep; 85():68-79. PubMed ID: 30026067
[TBL] [Abstract][Full Text] [Related]
17. Consistency of Feature Importance Algorithms for Interpretable EEG Abnormality Detection.
Knispel F; Brenner A; Röhrig R; Weber Y; Varghese J; Kutafina E
Stud Health Technol Inform; 2022 Aug; 296():33-40. PubMed ID: 36073486
[TBL] [Abstract][Full Text] [Related]
18. Generative machine learning for de novo drug discovery: A systematic review.
Martinelli DD
Comput Biol Med; 2022 Jun; 145():105403. PubMed ID: 35339849
[TBL] [Abstract][Full Text] [Related]
19. A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation.
Inbar O; Inbar O; Reuveny R; Segel MJ; Greenspan H; Scheinowitz M
Pulm Med; 2021; 2021():5516248. PubMed ID: 34158976
[TBL] [Abstract][Full Text] [Related]
20. MIDGET:Detecting differential gene expression on microarray data.
Angelescu R; Dobrescu R
Comput Methods Programs Biomed; 2021 Nov; 211():106418. PubMed ID: 34555591
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
