Zobrazeno 1 - 10
of 1 861
pro vyhledávání: '"RODRÍGUEZ, RAÚL"'
Publikováno v:
Universidad Peruana de Ciencias Aplicadas (UPC)Repositorio Académico - UPC.
Evento organizado por la Faculta de Comunicaciones, Programa Académico de Comunicación y Periodismo el 8 de abril de 2022. Lima, Perú.
Los infografistas Marco Hernández (New York Times), Raúl Rodríguez (El Comercio) y Manuel Amaya (El Come
Los infografistas Marco Hernández (New York Times), Raúl Rodríguez (El Comercio) y Manuel Amaya (El Come
Externí odkaz:
http://hdl.handle.net/10757/659543
Autor:
Dai, Yujie, Sullivan, Brian, Montout, Axel, Dillon, Amy, Waller, Chris, Acs, Peter, Denholm, Rachel, Williams, Philip, Hay, Alastair D, Santos-Rodriguez, Raul, Dowsey, Andrew
The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces significant challenges due to data heterogeneity, sparsity, temporal misalignment, and limited label
Externí odkaz:
http://arxiv.org/abs/2411.17645
Autor:
Clark, Jeffrey N., Wragg, Matthew, Nielsen, Emily, Perello-Nieto, Miquel, Keshtmand, Nawid, Ambler, Michael, Sharma, Shiv, Bourdeaux, Christopher P., Brigden, Amberly, Santos-Rodriguez, Raul
There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and less human-interpretable. This complexity raises concerns about trustw
Externí odkaz:
http://arxiv.org/abs/2411.11774
Intensive Care Units are complex, data-rich environments where critically ill patients are treated using variety of clinical equipment. The data collected using this equipment can be used clinical staff to gain insight into the condition of the patie
Externí odkaz:
http://arxiv.org/abs/2410.16959
Deep neural networks (DNNs) excel in tasks like image recognition and natural language processing, but their increasing complexity complicates deployment in resource-constrained environments and increases susceptibility to adversarial attacks. While
Externí odkaz:
http://arxiv.org/abs/2410.15176
The subjective quality of natural signals can be approximated with objective perceptual metrics. Designed to approximate the perceptual behaviour of human observers, perceptual metrics often reflect structures found in natural signals and neurologica
Externí odkaz:
http://arxiv.org/abs/2409.17069
Cutting-edge abstractive summarisers generate fluent summaries, but the factuality of the generated text is not guaranteed. Early summary factuality evaluation metrics are usually based on n-gram overlap and embedding similarity, but are reported fai
Externí odkaz:
http://arxiv.org/abs/2409.15090
Autor:
Sahota, Amarpal, Roguski, Amber, Jones, Matthew W, Abdallah, Zahraa S., Santos-Rodriguez, Raul
We evaluate the effectiveness of combining brain connectivity metrics with signal statistics for early stage Parkinson's Disease (PD) classification using electroencephalogram data (EEG). The data is from 5 arousal states - wakeful and four sleep sta
Externí odkaz:
http://arxiv.org/abs/2408.00711
Autor:
Clifford, Matt, Erskine, Jonathan, Hepburn, Alexander, Santos-Rodríguez, Raúl, Garcia-Garcia, Dario
Class imbalance poses a significant challenge in classification tasks, where traditional approaches often lead to biased models and unreliable predictions. Undersampling and oversampling techniques have been commonly employed to address this issue, y
Externí odkaz:
http://arxiv.org/abs/2407.11878
Quantifying a patient's health status provides clinicians with insight into patient risk, and the ability to better triage and manage resources. Early Warning Scores (EWS) are widely deployed to measure overall health status, and risk of adverse outc
Externí odkaz:
http://arxiv.org/abs/2407.09373