Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Ibrahim, Zina"'
Autor:
Qian, Linglong, Wang, Tao, Wang, Jun, Ellis, Hugh Logan, Mitra, Robin, Dobson, Richard, Ibrahim, Zina
We introduce a novel classification framework for time-series imputation using deep learning, with a particular focus on clinical data. By identifying conceptual gaps in the literature and existing reviews, we devise a taxonomy grounded on the induct
Externí odkaz:
http://arxiv.org/abs/2407.08442
Autor:
Du, Wenjie, Wang, Jun, Qian, Linglong, Yang, Yiyuan, Liu, Fanxing, Wang, Zepu, Ibrahim, Zina, Liu, Haoxin, Zhao, Zhiyuan, Zhou, Yingjie, Wang, Wenjia, Ding, Kaize, Liang, Yuxuan, Prakash, B. Aditya, Wen, Qingsong
Effective imputation is a crucial preprocessing step for time series analysis. Despite the development of numerous deep learning algorithms for time series imputation, the community lacks standardized and comprehensive benchmark platforms to effectiv
Externí odkaz:
http://arxiv.org/abs/2406.12747
In this study, we explore the impact of different masking strategies on time series imputation models. We evaluate the effects of pre-masking versus in-mini-batch masking, normalization timing, and the choice between augmenting and overlaying artific
Externí odkaz:
http://arxiv.org/abs/2405.17508
Missingness is ubiquitous in multivariate time series and poses an obstacle to reliable downstream analysis. Although recurrent network imputation achieved the SOTA, existing models do not scale to deep architectures that can potentially alleviate is
Externí odkaz:
http://arxiv.org/abs/2401.02258
Autor:
Qian, Linglong, Ibrahim, Zina, Ellis, Hugh Logan, Zhang, Ao, Zhang, Yuezhou, Wang, Tao, Dobson, Richard
This study presents a novel approach to addressing the challenge of missing data in multivariate time series, with a particular focus on the complexities of healthcare data. Our Conditional Self-Attention Imputation (CSAI) model, grounded in a transf
Externí odkaz:
http://arxiv.org/abs/2312.16713
Autor:
Wu, Jinge, Kim, Yunsoo, Keller, Eva C., Chow, Jamie, Levine, Adam P., Pontikos, Nikolas, Ibrahim, Zina, Taylor, Paul, Williams, Michelle C., Wu, Honghan
This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports. We created an evaluation dataset from real-world radiology datasets (including X-
Externí odkaz:
http://arxiv.org/abs/2312.13103
Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the overall care of a patient. Methods to automatically produce summaries
Externí odkaz:
http://arxiv.org/abs/2211.07126
Publikováno v:
JBI v124 (2021)
The current mode of use of Electronic Health Record (EHR) elicits text redundancy. Clinicians often populate new documents by duplicating existing notes, then updating accordingly. Data duplication can lead to a propagation of errors, inconsistencies
Externí odkaz:
http://arxiv.org/abs/2105.11832
Autor:
Ibrahim, Zina M, Bean, Daniel, Searle, Thomas, Wu, Honghan, Shek, Anthony, Kraljevic, Zeljko, Galloway, James, Norton, Sam, Teo, James T, Dobson, Richard JB
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes.
Externí odkaz:
http://arxiv.org/abs/2011.09361
Autor:
Kraljevic, Zeljko, Searle, Thomas, Shek, Anthony, Roguski, Lukasz, Noor, Kawsar, Bean, Daniel, Mascio, Aurelie, Zhu, Leilei, Folarin, Amos A, Roberts, Angus, Bendayan, Rebecca, Richardson, Mark P, Stewart, Robert, Shah, Anoop D, Wong, Wai Keong, Ibrahim, Zina, Teo, James T, Dobson, Richard JB
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis. We present the open-source Medical Concept Annotation Toolkit (MedCAT) that
Externí odkaz:
http://arxiv.org/abs/2010.01165