Latest AI and machine learning research in medicare for healthcare professionals.
Identifying cognitive impairment within electronic health records (EHRs) is crucial not only for timely diagnoses but also for facilitating research. Information about cognitive impairment often exists within unstructured clinician notes in EHRs, but manual chart reviews are both time-consuming and error-prone. To address this issue, our study evaluates an automated approach using zero-shot GPT-...
Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all treatment with limited success in persistent AF. This may be due to our inability to map the dynamics of AF with the limited resolution and coverage provided by sequential contact mapping catheters, preventing effective patient phenotyping for personalised, targeted ablation. Here we introduce FibMap, a graph recurrent...
The sharing of large-scale transportation data is beneficial for transportation planning and policymaking. However, it also raises significant secur...
Wide coverage and high-precision rural household wealth data is an important support for the effective connection between the national macro rural r...
Text-conditioned image generation has gained significant attention in recent years and are processing increasingly longer and comprehensive text pro...
We introduce Long-VITA, a simple yet effective large multi-modal model for long-context visual-language understanding tasks. It is adept at concurre...
The perspective of developing trustworthy AI for critical applications in science and engineering requires machine learning techniques that are capa...
The integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market ...
Human motion video generation has advanced significantly, while existing methods still struggle with accurately rendering detailed body parts like h...
TerraQ is a spatiotemporal question-answering engine for satellite image archives. It is a natural language processing system that is built to proce...
Recently computer-aided diagnosis has demonstrated promising performance, effectively alleviating the workload of clinicians. However, the inherent ...
3D articulated objects modeling has long been a challenging problem, since it requires to capture both accurate surface geometries and semantically ...
Large language models (LLMs) have shown impressive capabilities in natural language processing tasks, including dialogue generation. This research a...
Cognitive diagnosis can infer the students' mastery of specific knowledge concepts based on historical response logs. However, the existing cognitiv...
In recent years, Transformers-based models have made significant progress in the field of image restoration by leveraging their inherent ability to ...
Conformal prediction is an emerging technique for uncertainty quantification that constructs prediction sets guaranteed to contain the true label wi...
Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated predictio...
We introduce Qwen2.5-1M, a series of models that extend the context length to 1 million tokens. Compared to the previous 128K version, the Qwen2.5-1...
Integrated sensing and communication (ISAC) boosts network efficiency by using existing resources for diverse sensing applications. In this work, we...
Post-hoc calibration of pre-trained models is critical for ensuring reliable inference, especially in safety-critical domains such as healthcare. Co...