Zobrazeno 1 - 10
of 273
pro vyhledávání: '"Zheng, Xiaochen"'
Autor:
Zhu, Yinghao, Gao, Junyi, Wang, Zixiang, Liao, Weibin, Zheng, Xiaochen, Liang, Lifang, Wang, Yasha, Pan, Chengwei, Harrison, Ewen M., Ma, Liantao
The use of Large Language Models (LLMs) in medicine is growing, but their ability to handle both structured Electronic Health Record (EHR) data and unstructured clinical notes is not well-studied. This study benchmarks various models, including GPT-b
Externí odkaz:
http://arxiv.org/abs/2407.18525
Autor:
Zhu, Yinghao, Ren, Changyu, Wang, Zixiang, Zheng, Xiaochen, Xie, Shiyun, Feng, Junlan, Zhu, Xi, Li, Zhoujun, Ma, Liantao, Pan, Chengwei
The integration of multimodal Electronic Health Records (EHR) data has notably advanced clinical predictive capabilities. However, current models that utilize clinical notes and multivariate time-series EHR data often lack the necessary medical conte
Externí odkaz:
http://arxiv.org/abs/2406.00036
Autor:
Zheng, Xiaochen, Schürch, Manuel, Chen, Xingyu, Komninou, Maria Angeliki, Schüpbach, Reto, Allam, Ahmed, Bartussek, Jan, Krauthammer, Michael
The identification of phenotypes within complex diseases or syndromes is a fundamental component of precision medicine, which aims to adapt healthcare to individual patient characteristics. Postoperative delirium (POD) is a complex neuropsychiatric c
Externí odkaz:
http://arxiv.org/abs/2405.03327
Large Language Models (LLMs) have revolutionized natural language processing, but their robustness against adversarial attacks remains a critical concern. We presents a novel white-box style attack approach that exposes vulnerabilities in leading ope
Externí odkaz:
http://arxiv.org/abs/2405.02764
Autor:
Chen, Xingyu, Zheng, Xiaochen, Mollaysa, Amina, Schürch, Manuel, Allam, Ahmed, Krauthammer, Michael
Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e., features) and varying sampling rates (i.e., recordings/measurement) across these features. Modeli
Externí odkaz:
http://arxiv.org/abs/2311.07744
The digital transformation of pharmaceutical industry is a challenging task due to the high complexity of involved elements and the strict regulatory compliance. Maintenance activities in the pharmaceutical industry play an essential role in ensuring
Externí odkaz:
http://arxiv.org/abs/2310.15417
Autor:
Zhu, Yinghao, Wang, Zixiang, He, Long, Xie, Shiyun, Zheng, Xiaochen, Ma, Liantao, Pan, Chengwei
Electronic Health Records (EHRs) contain a wealth of patient data; however, the sparsity of EHRs data often presents significant challenges for predictive modeling. Conventional imputation methods inadequately distinguish between real and imputed dat
Externí odkaz:
http://arxiv.org/abs/2309.04160
Autor:
Burkert, Andreas, Gillessen, Stefan, Lin, Douglas N. C., Zheng, Xiaochen, Schoeller, Philipp, Eisenhauer, Frank, Genzel, Reinhard
The orbital distribution of the S-star cluster surrounding the supermassive black hole in the center of the Milky Way is analyzed. A tight, roughly exponential dependence of the pericenter distance r$_{p}$ on orbital eccentricity e$_{\star}$ is found
Externí odkaz:
http://arxiv.org/abs/2306.02076
Autor:
Zheng, Xiaochen, Chen, Xingyu, Schürch, Manuel, Mollaysa, Amina, Allam, Ahmed, Krauthammer, Michael
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these methods are less effective for time series forecasting, as optimization of instance discriminati
Externí odkaz:
http://arxiv.org/abs/2303.18205
Autor:
Zheng, Xiaochen
Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on supervised learning and thus require vast amounts of training data. Due to their scarcity and minuscule size, ann
Externí odkaz:
http://arxiv.org/abs/2211.05636