Latest AI and machine learning research in health policy for healthcare professionals.
Diffusion policies have shown promise in learning complex behaviors from demonstrations, particularly for tasks requiring precise control and long-term planning. However, they face challenges in robustness when encountering distribution shifts. This paper explores improving diffusion-based imitation learning models through online interactions with the environment. We propose OTPR (Optimal Transp...
Autonomous robotic wiping is an important task in various industries, ranging from industrial manufacturing to sanitization in healthcare. Deep reinforcement learning (Deep RL) has emerged as a promising algorithm, however, it often suffers from a high demand for repetitive reward engineering. Instead of relying on manual tuning, we first analyze the convergence of quality-critical robotic wipin...
Problem: How can we design patient cost prediction models that effectively address the challenges of heterogeneity in administrative claims (AC) dat...
Pulmonary Tuberculosis (PTB) remains a major challenge for global health, especially in areas with poor medical resources, where access to specializ...
Deep Reinforcement Learning (RL) is remarkably effective in addressing sequential resource allocation problems in domains such as healthcare, public...
Access to health resources is a critical determinant of public well-being and societal resilience, particularly during public health crises when dem...
Large Language Models (LLMs) employ three popular training approaches: Masked Language Models (MLM), Causal Language Models (CLM), and Sequence-to-S...
Rising provider turnover forces healthcare administrators to frequently rematch patients to available providers, which can be cumbersome and labor-i...
Automated colonoscopy reporting holds great potential for enhancing quality control and improving cost-effectiveness of colonoscopy procedures. A ma...
Background: Computed Tomography Angiography (CTA) is crucial for cerebrovascular disease diagnosis. Dynamic CTA is a type of imaging that captures t...
Genetic data collection has become ubiquitous today. The ability to meaningfully interpret genetic data has motivated its widespread use, providing ...
Accessing the internet in regions with expensive data plans and limited connectivity poses significant challenges, restricting information access an...
Legal reform can be challenging in light of the volume, complexity, and interdependence of laws, codes, and records. One salient example of this cha...
Background: Data collected in controlled settings typically results in high-quality datasets. However, in real-world applications, the quality of da...
In this technical report, we present Magic 1-For-1 (Magic141), an efficient video generation model with optimized memory consumption and inference l...
Continual learning (CL) has traditionally emphasized minimizing exemplar memory usage, assuming that memory is the primary bottleneck. However, in m...
In decision-making systems, algorithmic recourse aims to identify minimal-cost actions to alter an individual features, thereby obtaining a desired ...
Multilevel Monte Carlo (MLMC) reduces the total computational cost of financial option pricing by combining SDE approximations with multiple resolut...
Machine learning (ML) transforms healthcare by enabling predictive analytics, personalized treatments, and improved patient outcomes. However, tradi...
Differentially private (DP) synthetic data is a versatile tool for enabling the analysis of private data. Recent advancements in large language mode...