Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Yin Changchang"'
Autor:
Wu, Siyi, Cao, Weidan, Fu, Shihan, Yao, Bingsheng, Yang, Ziqi, Yin, Changchang, Mishra, Varun, Addison, Daniel, Zhang, Ping, Wang, Dakuo
Despite recent advances in cancer treatments that prolong patients' lives, treatment-induced cardiotoxicity remains one severe side effect. The clinical decision-making of cardiotoxicity is challenging, as non-clinical symptoms can be missed until li
Externí odkaz:
http://arxiv.org/abs/2410.04592
Autor:
Wu, Siyi, Cao, Weidan, Fu, Shihan, Yao, Bingsheng, Yang, Ziqi, Yin, Changchang, Mishra, Varun, Addison, Daniel, Zhang, Ping, Wang, Dakuo
Cardiotoxicity induced by cancer treatment has become a major clinical concern, affecting the long-term survival and quality of life of cancer patients. Effective clinical decision-making, including the detection of cancer treatment-induced cardiotox
Externí odkaz:
http://arxiv.org/abs/2408.03586
Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the survival of sepsis patients. Existing predictive models are usually trained on high-quality data with few mis
Externí odkaz:
http://arxiv.org/abs/2407.16999
Large Vision Language Models (LVLMs) have recently achieved superior performance in various tasks on natural image and text data, which inspires a large amount of studies for LVLMs fine-tuning and training. Despite their advancements, there has been
Externí odkaz:
http://arxiv.org/abs/2407.02730
The adoption of large language models (LLMs) in healthcare has attracted significant research interest. However, their performance in healthcare remains under-investigated and potentially limited, due to i) they lack rich domain-specific knowledge an
Externí odkaz:
http://arxiv.org/abs/2405.11640
Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are receiving increasing attention in healthcare. An effective representation of a patient's EHR should hierarchically encompass both the temporal relationships betwee
Externí odkaz:
http://arxiv.org/abs/2405.03943
Autor:
Zhang, Shao, Yu, Jianing, Xu, Xuhai, Yin, Changchang, Lu, Yuxuan, Yao, Bingsheng, Tory, Melanie, Padilla, Lace M., Caterino, Jeffrey, Zhang, Ping, Wang, Dakuo
Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires
Externí odkaz:
http://arxiv.org/abs/2309.12368
Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment regime (DTR) with reinforcement learning (RL) on electronic health recor
Externí odkaz:
http://arxiv.org/abs/2205.09852
Disease progression modeling (DPM) involves using mathematical frameworks to quantitatively measure the severity of how certain disease progresses. DPM is useful in many ways such as predicting health state, categorizing disease stages, and assessing
Externí odkaz:
http://arxiv.org/abs/2109.14147
Complication risk profiling is a key challenge in the healthcare domain due to the complex interaction between heterogeneous entities (e.g., visit, disease, medication) in clinical data. With the availability of real-world clinical data such as elect
Externí odkaz:
http://arxiv.org/abs/2109.12276