Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Glass, Lucas M."'
Autor:
Wu, Zhenbang, Yao, Huaxiu, Su, Zhe, Liebovitz, David M, Glass, Lucas M, Zou, James, Finn, Chelsea, Sun, Jimeng
Drug recommendation assists doctors in prescribing personalized medications to patients based on their health conditions. Existing drug recommendation solutions adopt the supervised multi-label classification setup and only work with existing drugs w
Externí odkaz:
http://arxiv.org/abs/2210.05572
A clinical trial is an essential step in drug development, which is often costly and time-consuming. In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials. AI-enabled
Externí odkaz:
http://arxiv.org/abs/2209.09023
Given a deep learning model trained on data from a source site, how to deploy the model to a target hospital automatically? How to accommodate heterogeneous medical coding systems across different hospitals? Standard approaches rely on existing medic
Externí odkaz:
http://arxiv.org/abs/2203.02446
Population-level disease prediction estimates the number of potential patients of particular diseases in some location at a future time based on (frequently updated) historical disease statistics. Existing approaches often assume the existing disease
Externí odkaz:
http://arxiv.org/abs/2202.03415
Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine lear
Externí odkaz:
http://arxiv.org/abs/2105.01171
Clinical trials are crucial for drug development but are time consuming, expensive, and often burdensome on patients. More importantly, clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment.
Externí odkaz:
http://arxiv.org/abs/2102.04252
Autor:
Kargas, Nikos, Qian, Cheng, Sidiropoulos, Nicholas D., Xiao, Cao, Glass, Lucas M., Sun, Jimeng
Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to predict the evolution of epidemic trends for many regions simultaneou
Externí odkaz:
http://arxiv.org/abs/2012.04747
To test the possibility of differentiating chest x-ray images of COVID-19 against other pneumonia and healthy patients using deep neural networks. We construct the X-ray imaging data from two publicly available sources, which include 5508 chest x-ray
Externí odkaz:
http://arxiv.org/abs/2010.16039
Successful health risk prediction demands accuracy and reliability of the model. Existing predictive models mainly depend on mining electronic health records (EHR) with advanced deep learning techniques to improve model accuracy. However, they all ig
Externí odkaz:
http://arxiv.org/abs/2010.11389
Autor:
Huang, Kexin, Fu, Tianfan, Khan, Dawood, Abid, Ali, Abdalla, Ali, Abid, Abubakar, Glass, Lucas M., Zitnik, Marinka, Xiao, Cao, Sun, Jimeng
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable progress in predicting drug efficacy. We develop MolDesigner, a human-in-the-loop web user-interface
Externí odkaz:
http://arxiv.org/abs/2010.03951