Latest AI and machine learning research in medicare for healthcare professionals.
To develop privacy-enhancing statistical methods for estimation of binary disease risk model association parameters across multiple electronic health record (EHR) sites with heterogeneous selection mechanisms, without sharing raw individual-level data. We illustrate their utility through a cross-biobank analysis of smoking and 97 cancer subtypes using data from the NIH All of Us (AOU) and the Mich...
Long COVID affects 10-40% of COVID-19 survivors, yet early detection remains challenging. We present TACO (TabPFN Augmented Causal Outcomes), a framework that uniquely combines causal inference with foundation models for presymptomatic Long COVID detection. TACO employs Differential Causal Effect (DCE) analysis to identify causally relevant genes, then utilizes TabPFN, a foundation model that does...
To prospectively evaluate the feasibility and performance of expedited screening mammogram interpretation for women identified as high-risk by a deep ...
Clinical and population decision-making relies on the systematic evaluation of extensive regulatory evidence. The FDA drug reviews provide detailed in...
Accurate dose prediction is essential for automating radiotherapy planning. In spot scanning proton therapy (SSPT), dose evaluation is required at bot...
Explainable AI (XAI) is essential in clinical machine learning, yet quantitative evaluation of explanation quality is rarely reported in a reproducibl...
The adoption of artificial intelligence in dermatology promises democratized access to healthcare, but model reliability depends on the quality and co...
Early prediction of in-hospital death remains a significant challenge due to the limited availability of structured data during initial admission. Uns...
Develop and deploy a real-time, EHR-integrated machine learning phenotype to identify emergency department (ED) patients with opioid use disorder (OUD...
An effective Multi-Agent Path Finding (MAPF) algorithm must efficiently plan paths for multiple agents while adhering to constraints, ensuring safe na...
PURPOSE: We aim to evaluate various proxy selection methods within the context of high-dimensional propensity score (hdPS) analysis. This study aimed ...
In post-disaster scenarios, effective rescue operations hinge on deploying robots equipped with sophisticated path planning algorithms capable of navi...
In many applications, such as coverage exploration and search and rescue missions, accurately assessing environmental complexity is valuable for perfo...
The editorial, "Clinical and translational mode of single-cell measurements: An artificial intelligent single-cell," introduces the innovative clinica...
Biomedical imaging modalities often produce high-resolution, multi-dimensional images that pose computational challenges for deep neural networks. T...
Large language models (LLMs) are empowering decision-making in several applications, including tool or API usage and answering multiple-choice quest...
Our aim is to learn to solve long-horizon decision-making problems in highly-variable, combinatorially-complex robotics domains given raw sensor inp...
Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease man...
Long COVID continues to challenge public health by affecting a significant segment of individuals who have recovered from acute SARS-CoV-2 infection...
An image may convey a thousand words, but a video composed of hundreds or thousands of image frames tells a more intricate story. Despite significan...