Treffer 81 - 100 von 15.425

dRFEtools: dynamic recursive feature elimination for omics.
Benjamin KJM ; Katipalli T ; Paquola ACM
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE

Estimação Tridimensional da Pose e Forma Humana para Sistemas Autónomos de Telerreabilitação
Varandas, Miguel Ângelo Soares Cerveira

Dieses Ergebnis kann Gästen nicht angezeigt werden.
Login für vollen Zugriff.

What Are the Differences between Student and ChatGPT-Generated Pseudocode? Detecting AI-Generated Pseudocode in High School Programming Using Explainable Machine Learning
Zifeng Liu ; Wanli Xing ; Xinyue Jiao ; et al.
40

PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach (PEPPERMINT)
Britta Trautwein, Resident doctor
PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach
Fernandez-Bustamante A, Frendl G, Sprung J, Kor DJ, Subramaniam B, Martinez Ruiz R, Lee JW, Henderson WG, Moss A, Mehdiratta N, Colwell MM, Bartels K, Kolodzie K, Giquel J, Vidal Melo MF. Postoperative Pulmonary Complications, Early Mortality, and Hospital Stay Following Noncardiothoracic Surgery: A Multicenter Study by the Perioperative Research Network Investigators. JAMA Surg. 2017 Feb 1;152(2):157-166. doi: 10.1001/jamasurg.2016.4065.
Ferreyra GP, Baussano I, Squadrone V, Richiardi L, Marchiaro G, Del Sorbo L, Mascia L, Merletti F, Ranieri VM. Continuous positive airway pressure for treatment of respiratory complications after abdominal surgery: a systematic review and meta-analysis. Ann Surg. 2008 Apr;247(4):617-26. doi: 10.1097/SLA.0b013e3181675829.
Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth. 2017 Mar 1;118(3):317-334. doi: 10.1093/bja/aex002.
Abbott TEF, Fowler AJ, Pelosi P, Gama de Abreu M, Moller AM, Canet J, Creagh-Brown B, Mythen M, Gin T, Lalu MM, Futier E, Grocott MP, Schultz MJ, Pearse RM; StEP-COMPAC Group. A systematic review and consensus definitions for standardised end-points in perioperative medicine: pulmonary complications. Br J Anaesth. 2018 May;120(5):1066-1079. doi: 10.1016/j.bja.2018.02.007. Epub 2018 Mar 27.
Ball L, Pelosi P. Predictive scores for postoperative pulmonary complications: time to move towards clinical practice. Minerva Anestesiol. 2016 Mar;82(3):265-7. Epub 2015 Sep 4. No abstract available.
Nithiuthai J, Siriussawakul A, Junkai R, Horugsa N, Jarungjitaree S, Triyasunant N. Do ARISCAT scores help to predict the incidence of postoperative pulmonary complications in elderly patients after upper abdominal surgery? An observational study at a single university hospital. Perioper Med (Lond). 2021 Dec 8;10(1):43. doi: 10.1186/s13741-021-00214-3.
Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications. JAMA Netw Open. 2021 Mar 1;4(3):e212240. doi: 10.1001/jamanetworkopen.2021.2240.
Szabo M, Bozo A, Darvas K, Soos S, Ozse M, Ivanyi ZD. The role of ultrasonographic lung aeration score in the prediction of postoperative pulmonary complications: an observational study. BMC Anesthesiol. 2021 Jan 14;21(1):19. doi: 10.1186/s12871-021-01236-6.
van Sloun RJG, Demi L. Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, In-Vivo Results. IEEE J Biomed Health Inform. 2020 Apr;24(4):957-964. doi: 10.1109/JBHI.2019.2936151. Epub 2019 Aug 19.
Brusasco C, Santori G, Tavazzi G, Via G, Robba C, Gargani L, Mojoli F, Mongodi S, Bruzzo E, Tro R, Boccacci P, Isirdi A, Forfori F, Corradi F; UCARE (Ultrasound in Critical care and Anesthesia Research Group). Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema. J Clin Monit Comput. 2022 Feb;36(1):131-140. doi: 10.1007/s10877-020-00629-1. Epub 2020 Dec 12.
Trautwein B, Beer M, Blobner M, Jungwirth B, Kagerbauer SM, Gotz M. Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. PLoS One. 2025 Aug 19;20(8):e0329076. doi: 10.1371/journal.pone.0329076. eCollection 2025.

Machine-Learning Based EEG Biomarkers for Personalized Interventions (EEG-INSIGHT)
Castellers de la Vila de Gràcia ; Dolors Soler, PhD, Principal Investigator
Machine-Learning Based EEG Biomarkers for Personalized Interventions
Gewandter JS, McDermott MP, Evans S, Katz NP, Markman JD, Simon LS, Turk DC, Dworkin RH. Composite outcomes for pain clinical trials: considerations for design and interpretation. Pain. 2021 Jul 1;162(7):1899-1905. doi: 10.1097/j.pain.0000000000002188. No abstract available.
Bikson M, Grossman P, Thomas C, Zannou AL, Jiang J, Adnan T, Mourdoukoutas AP, Kronberg G, Truong D, Boggio P, Brunoni AR, Charvet L, Fregni F, Fritsch B, Gillick B, Hamilton RH, Hampstead BM, Jankord R, Kirton A, Knotkova H, Liebetanz D, Liu A, Loo C, Nitsche MA, Reis J, Richardson JD, Rotenberg A, Turkeltaub PE, Woods AJ. Safety of Transcranial Direct Current Stimulation: Evidence Based Update 2016. Brain Stimul. 2016 Sep-Oct;9(5):641-661. doi: 10.1016/j.brs.2016.06.004. Epub 2016 Jun 15.
Zhdanov A, Atluri S, Wong W, Vaghei Y, Daskalakis ZJ, Blumberger DM, Frey BN, Giacobbe P, Lam RW, Milev R, Mueller DJ, Turecki G, Parikh SV, Rotzinger S, Soares CN, Brenner CA, Vila-Rodriguez F, McAndrews MP, Kleffner K, Alonso-Prieto E, Arnott SR, Foster JA, Strother SC, Uher R, Kennedy SH, Farzan F. Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression. JAMA Netw Open. 2020 Jan 3;3(1):e1918377. doi: 10.1001/jamanetworkopen.2019.18377.
Vuckovic A, Gallardo VJF, Jarjees M, Fraser M, Purcell M. Prediction of central neuropathic pain in spinal cord injury based on EEG classifier. Clin Neurophysiol. 2018 Aug;129(8):1605-1617. doi: 10.1016/j.clinph.2018.04.750. Epub 2018 May 23.
Mussigmann T, Bardel B, Lefaucheur JP. Resting-state electroencephalography (EEG) biomarkers of chronic neuropathic pain. A systematic review. Neuroimage. 2022 Sep;258:119351. doi: 10.1016/j.neuroimage.2022.119351. Epub 2022 Jun 2.
Mari T, Henderson J, Maden M, Nevitt S, Duarte R, Fallon N. Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data. J Pain. 2022 Mar;23(3):349-369. doi: 10.1016/j.jpain.2021.07.011. Epub 2021 Aug 21.

CellProfiler Analyst 3.0: accessible data exploration and machine learning for image analysis.
Stirling DR ; Carpenter AE ; Cimini BA
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE

PyCloze: Individualized cloze questions generated in Python for Moodle tests ; PyCloze: Questions cloze individualisées générées en Python pour les tests Moodle
Ghiaus, Christian ; Tillenkamp, Frank ; Centre d'Energétique et de Thermique de Lyon (CETHIL) ; et al.
7e Colloque Pédagogie et Formation
https://hal.science/hal-04413467
7e Colloque Pédagogie et Formation, INSA Hauts-de-France, May 2021, Valenciennes, France
https://colloqinsa2021.sciencesconf.org

A Deep Learning-Based Framework for Sentiment and Emotion Classification of Social Media Messages During Pandemic Periods.
Yu, Feng ; Liu, Jian Ming
Journal of Circuits, Systems & Computers; 2/15/2025, Vol. 35 Issue 3, p1-32, 32p

Predictive Analytics for Student Online Learning Performance Using Machine Learning and Data Mining Techniques.
ZI XIANG POH ; EAN TENG KHOR
International Journal on E-Learning. 2024, Vol. 23 Issue 3, p269-283. 15p.

Life cycle assessment using machine learning
Gomes, Sofia ; Faria, Brígida Mónica ; Oliveira, Alexandra Alves ; et al.

Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network
Kozlenko, Mykola ; Zamikhovska, Olena ; Zamikhovskyi, Leonid
Eastern-European Journal of Enterprise Technologies, vol. 2, no. 2(110), pp. 99-109, Apr. 2021

Tidal Corrections From and for SWOT Using a Spatially Coherent Variational Bayesian Harmonic Analysis.
Monahan, Thomas ; Tang, Tianning ; Roberts, Stephen ; et al.
Journal of Geophysical Research. Oceans. Mar2025, Vol. 130 Issue 3, p1-31. 31p.

GANerAid: Realistic synthetic patient data for clinical trials
Krenmayr, Lucas ; Frank, Roland ; Drobig, Christina ; et al.
In Informatics in Medicine Unlocked 2022 35

Some Pattern Recognitions for a Recommendation Framework for Higher Education Students' Generic Competence Development Using Machine Learning
So, Joseph Chi-ho ; Wong, Adam Ka-lok ; Tsang, Kia Ho-yin ; et al.
12

SOME PATTERN RECOGNITIONS FOR A RECOMMENDATION FRAMEWORK FOR HIGHER EDUCATION STUDENTS' GENERIC COMPETENCE DEVELOPMENT USING MACHINE LEARNING.
Chi-ho So, Joseph ; Ka-lok Wong, Adam ; Kia Ho-yin Tsang ; et al.
Journal of Technology & Science Education. 2023, Vol. 13 Issue 1, p104-115. 12p.

Análise do Movimento dos Atletas em Eventos Futebolísticos
Campos, João Manuel Costa ; Martins, António Constantino Lopes ; REPOSITÓRIO P.PORTO
urn:tid:203330897

Comparative analysis of machine learning algorithms based on an air pollution prediction model
Aneta Wiktorzak ; Bartosz Kaczorowski
TASK Quarterly, Vol 26, Iss 4 (2022)

MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks
Sil C. van de Leemput ; orcid:0000-0001-6047- ; Jonas Teuwen ; et al.

ClassiPyGRB: Machine Learning-Based Classification and Visualization of Gamma Ray Bursts using t-SNE
Garcia-Cifuentes, Keneth ; Becerra, Rosa L. ; De Colle, Fabio