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Found 10 papers, 4 papers with code
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs).
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records
Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving.
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts.
Autonomous Soft Tissue Retraction Using Demonstration-Guided Reinforcement Learning
Furthermore, we investigate the soft tissue interactions facilitated by the patient-side manipulator of the DaVinci surgical robot.
Nonlinear Causal Discovery via Kernel Anchor Regression
Anchor regression has been developed to address this problem for a large class of causal graphical models, though the relationships between the variables are assumed to be linear.
Multi-user Co-inference with Batch Processing Capable Edge Server
To deal with the coupled offloading and scheduling introduced by concurrent batch processing, we first consider an offline problem with a constant edge inference latency and the same latency constraint.
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review
This systematic review examines the use of Explainable Artificial Intelligence (XAI) during the pandemic and how its use could overcome barriers to real-world success.
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
Then, a greedy device scheduling algorithm is introduced, which in each step selects the device consuming the least updating time obtained by the optimal bandwidth allocation, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy.
Device Scheduling with Fast Convergence for Wireless Federated Learning
In each iteration of FL (called round), the edge devices update local models based on their own data and contribute to the global training by uploading the model updates via wireless channels.
Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning
Since the output data size of a DNN layer can be larger than that of the raw data, offloading intermediate data between layers can suffer from high transmission latency under limited wireless bandwidth.
