Latest AI and machine learning research in bioterrorism for healthcare professionals.
BackgroundRapid emergence and replacement of SARS-CoV-2 variants underscore the need for early and reliable indicators of variant dominance to guide timely public health response. However, early genomic trajectories are typically short, sparse, and noisy, with strong fluctuations and substantial cross-country heterogeneity in sequencing intensity and reporting. MethodsWe develop a scalable foreca...
The problem of corrupted data, missing features, or missing modalities continues to plague the modern machine learning landscape. To address this issue, a class of regularization methods that enforce consistency between imputed and fully observed data has emerged as a promising approach for improving model generalization, particularly in partially observed settings. We refer to this class of metho...
Selection of systemic therapy for breast cancer remains largely empirical, particularly for chemotherapy, due to the lack of robust biomarkers that pr...
Accurate prediction of drug response in precision medicine requires models that capture how specific chemical substructures interact with cellular pat...
BackgroundSystemic infections are a leading cause of hospitalization and death among patients with cirrhosis. Timely and accurate infection identifica...
The growth of generative AI and easily available Open Access health datasets has transformed researcher productivity, leading to an explosion in publi...
The direction of anticancer therapies has changed in recent years, including the increasing use of immunotherapy. However, around 50Â % of non-small-ce...
Medical physics and clinical engineering (MPCE) professionals have a critical role in the safe and effective deployment of artificial intelligence (AI...
Traditional simulator-based training for maritime professionals is critical for ensuring safety at sea but often depends on subjective trainer asses...
Assessing multi-hazard susceptibility and understanding community insights are important for effective disaster risk management; however, limited rese...
Storm surge forecasting plays a crucial role in coastal disaster preparedness, yet existing machine learning approaches often suffer from limited sp...
Multi-session persona-based dialogue generation presents challenges in maintaining long-term consistency and generating diverse, personalized respon...
Large Language Models (LLMs) currently respond to every prompt. However, they can produce incorrect answers when they lack knowledge or capability -...
We study a common challenge in reinforcement learning for large language models (LLMs): the Zero-Reward Assumption, where non-terminal actions (i.e....
A mean field method for pulse-coupled oscillators with delays used a self-connected oscillator to represent a synchronous cluster of - 1 oscillators ...
Platinum-based therapy is an integral part of the standard treatment for ovarian cancer. However, despite extensive research spanning several decades,...
Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer (NSCLC) remains a critical unmet need. Existing radiomics and deep learni...
This paper investigates the integration of response time data into human preference learning frameworks for more effective reward model elicitation....
We study the problem of minimizing non-convex functionals on the space of probability measures, regularized by the relative entropy (KL divergence) ...
Accurate diabetes risk prediction relies on identifying key features from complex health datasets, but conventional methods like mutual information ...