Latest AI and machine learning research in health policy for healthcare professionals.
The transformative potential of AI in healthcare - including better diagnostics, treatments, and expanded access - is currently limited by siloed patient data across multiple systems. Federal initiatives are necessary to provide critical infrastructure for health data repositories for data sharing, along with mechanisms to enable access to this data for appropriately trained computing researcher...
Large Language Models (LLMs) have fundamentally transformed approaches to Natural Language Processing (NLP) tasks across diverse domains. In healthcare, accurate and cost-efficient text classification is crucial, whether for clinical notes analysis, diagnosis coding, or any other task, and LLMs present promising potential. Text classification has always faced multiple challenges, including manua...
Universal healthcare access is critically needed, especially in resource-limited settings. Large Language Models (LLMs) offer promise for democratiz...
Offline reinforcement learning (RL) methods aim to learn optimal policies with access only to trajectories in a fixed dataset. Policy constraint met...
Integrating blockchain technology into healthcare systems presents a transformative approach to documenting, storing, and accessing electronic healt...
Meta-learning has been proposed as a promising machine learning topic in recent years, with important applications to image classification, robotics...
The widespread adoption of artificial intelligence (AI) tools in academic settings has the potential to revolutionize learning experiences, enhance ed...
Practical control systems pose significant challenges in identifying optimal control policies due to uncertainties in the system model and external ...
Over the past decade, the Table Union Search (TUS) task has aimed to identify unionable tables within data lakes to improve data integration and dis...
In the current paper, we will focus on requirements to ensure big data can advance the outcomes of our patients suffering from kidney disease. The ass...
Omnidirectional image, also called 360-degree image, is able to capture the entire 360-degree scene, thereby providing more realistic immersive feel...
Low-cost accelerometers play a crucial role in modern society due to their advantages of small size, ease of integration, wearability, and mass prod...
With the advent of Vision-Language Models (VLMs), medical artificial intelligence (AI) has experienced significant technological progress and paradi...
Electronic Health Records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity present significant ch...
Sepsis is a major cause of ICU mortality, where early recognition and effective interventions are essential for improving patient outcomes. However,...
We investigate an emerging setup in which a small, on-device language model (LM) with access to local data communicates with a frontier, cloud-hoste...
Access to sexual and reproductive health information remains a challenge in many communities globally, due to cultural taboos and limited availabili...
The rapid growth of deploying machine learning (ML) models within embedded systems on a chip (SoCs) has led to transformative shifts in fields like ...
The complexity of scenes and variations in image quality result in significant variability in the performance of semantic segmentation methods of re...
The effective and targeted provision of health information to consumers, specifically tailored to their needs and preferences, is indispensable in h...