Results 231 to 240 of about 212,260 (313)

Can epilepsy be predicted after the first febrile seizure? Insights from machine learning of postictal EEG

Epileptic Disorders, EarlyView.
Abstract Objective Febrile seizures (FS) are the most common seizures in childhood, yet identifying children at risk of developing epilepsy after the first FS remains challenging. We aimed to evaluate the prognostic potential of machine learning (ML) algorithms applied to post‐febrile seizure electroencephalography (EEG) recordings.
Boran Şekeroğlu, Hüseyin Öztoprak, Özlem Yayıcı Köken, Hatice Demir, Mehpare Sarı Yanartaş, Deniz Yılmaz, Burçin Şanlıdağ, Ayşegül Neşe Çıtak Kurt   +7 more
wiley   +1 more source

Concrete Crack Detection and Classification Methods Based on Machine Vision and Deep Learning. [PDF]

Sensors (Basel)
Chen W, Peng Z, Chen X, Zhao L, Xu T, Li Q, Huang X, Kannangara KKPM.   +7 more
europepmc   +1 more source

A metric learning perspective of SVM: on the relation of LMNN and SVM.

Journal of Machine Learning Research - Proceedings Track, 2012
Do Huyen, Kalousis Alexandros, Wang Jun, Woznica Adam   +3 more
openaire   +2 more sources

Maternal and umbilical cord plasma concentrations of antiseizure medications: Results from the observational MONEAD study

Epilepsia, EarlyView.
Abstract Objective Unanticipated changes in antiseizure medication (ASM) exposure can lead to subtherapeutic or toxic medication concentrations in the mother and unnecessary drug exposure for the fetus. The objectives of this study were to characterize ASM concentrations in mother's and cord blood at delivery in women with epilepsy (PWWE).
Charul Avachat, Kimford J. Meador, Page B. Pennell, Angela K. Birnbaum, MONEAD Investigator Group, Delmaris Acosta‐Cotte, Sandra Alhaj, Stephanie Allien, Taimur Anwar, Anto Bagic, Gregory L. Barkley, Donald Bearden, Susan Beers, Irena Bellinski, Christin Bermudez, Kristina Blessing, Katrina Boyer, Camilla Casadei, Patricia Chang, Li Chen, Andrea Cheng‐Hakimian, Melanie Choe, Kirsten Cleary, Tobi Clements, Joseph Coda, Pam Coe, Jules Constantinou, Yael Cukier Cukier, Danielle Culbreth, Elizabeth Cunningham, Kayla Darris, Lisa Davis, Rosemarie Delucca, Jennifer DeWolfe, Jessica Dimos, Mary Dolan, Maurice Druzin, Joyce Echo, Sarah Ellis, Pedro Figueredo, Richard Finnell, Kellie Flood‐Schaffer, Jacqueline French, Mark Friedman, Shailaja Gaddam, Satya Gedela, Elizabeth Garard, Christine Ghilian, William Grobman, Cheryl Hall, Ellen Hanson, Jacqueline Helcer Helcer, Paige Hickey, Gregory Holmes, Theresa Holmes, Dominic Ippolito, George Jewell, Arundhathi Jeyabalan, Emily Johnson, Michelle Kim, Gregory Krauss, Casey Krueger, David Labiner, Hadley Lange, Erin Latif, Connie Lau Lau, Shari Lawson, Brenda Leung, William MacAllister, James Maciulla, Hayley Madeiros, Nazin Mahmood, Jennie Mao, Ryan May, Paul McCabe, Frederick T. McElrath, Erica Meltzer Meltzer, Lucy Mendoza, Emily Miller, John W. Miller, Michelle Miranda, Jennifer Moon, Eugene Moore, Melissa Morris, Chris Morrison, Lorene Nelson, Melanee Newman, Alisha Olson, Kim Ono, John Owen, Alison Pack, Michael Paglia, Yong Park, Lamar Parker, Christina Patterson, Sonia Perez, Jenny Pohlman, Alison Pritchard, Michael Privitera, Krestin Radonovich, Patty Ray, Katie Reger, Gustavo Rey, Matthew Ryan, Yasin Salih, Carla Sandles, William Schweizer, Jordan Seliger, Enrique Serrano, Nilay Shah, Elizabeth Shashkova, Traci Sheer, Yvonne Sheldon, Rachel Sierra, Marianna Spanaki‐Varelas, Anna Steele, Jennifer Steele, Alice Stek, Zachary Stowe, Jolie Strauss, Suzanne Strickland, Melissa Sutcliffe, Hima Bindu Tam Tam, Diane Teagarden, Andrea Thomas, Matthew Thompson, Jeffery Tsai, Alexandra Urban, Linda Van Marter, Naymee Velez‐Ruiz, Yue Wang, Vibhangini Wasade, Taylor Weinau, Peter Wells, Carrie Wiles, Mark Yerby, Amy Young, Andrew Zillgitt, Annette Zygmunt   +138 more
wiley   +1 more source

Tech-based evaluation of healthcare quality during the COVID-19 pandemic. [PDF]

Medicine (Baltimore)
Wang K, Xu R, Huang Q.
europepmc   +1 more source

Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta‐analysis of four clinical applications

Epilepsia, EarlyView.
Abstract Objective The application of artificial intelligence/machine learning (AI/ML) to magnetic resonance imaging (MRI) promises to enhance and support clinical decision‐making in epilepsy. However, there currently lacks an appropriate assessment of clinical utility and study rigor of current AI/ML‐driven models that are targeted toward supporting ...
Judy Chen, Ella Sahlas, Yigu Zhou, Natalie Chen, Jim Xie, Farhan Wadia, Lorenzo Caciagli, Aristides Hadjinicolaou, Alexander G. Weil, Roy W. Dudley, Dewi V. Schrader, Andrea Bernasconi, Neda Bernasconi, Boris C. Bernhardt   +13 more
wiley   +1 more source

Development of a Machine Learning-Based Predictive Model for Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma. [PDF]

Cancer Med
Li T, Butler TO, Hu Y, Li S.
europepmc   +1 more source

Effects of fenfluramine and sigma‐1‐dependent pharmacological and genetic modulation in a mouse kindling model

Epilepsia, EarlyView.
Abstract Objective Sigma‐1 is a chaperone protein that serves as a key homeostatic regulator, implicated in neuronal excitability and seizure control. Positive allosteric modulators offer a use‐dependent means to enhance Sigma‐1 activity, potentially with favorable tolerability compared to direct agonists.
Eva‐Lotta von Rüden, Verena Buchecker, Amelie Wagner, Victoria M. Stocker, Liga Zvejniece, Heidrun Potschka   +5 more
wiley   +1 more source

Prediction of atelectasis in <i>Mycoplasma pneumoniae</i> pneumonia using a SHapley Additive exPlanations-interpretable machine learning model. [PDF]

Front Pediatr
Sun J, Wang T, Li M, Wang M.
europepmc   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source