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Found 12 papers, 5 papers with code
Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients
Medical documentation, including discharge notes, is crucial for ensuring patient care quality, continuity, and effective medical communication.
InMD-X: Large Language Models for Internal Medicine Doctors
In this paper, we introduce InMD-X, a collection of multiple large language models specifically designed to cater to the unique characteristics and demands of Internal Medicine Doctors (IMD).
NOTE: Notable generation Of patient Text summaries through Efficient approach based on direct preference optimization
The discharge summary is a one of critical documents in the patient journey, encompassing all events experienced during hospitalization, including multiple visits, medications, tests, surgery/procedures, and admissions/discharge.
UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge
Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models.
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning
To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks.
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
EHR systems lack a unified code system forrepresenting medical concepts, which acts asa barrier for the deployment of deep learningmodels in large scale to multiple clinics and hos-pitals.
Deep reinforcement learning for guidewire navigation in coronary artery phantom
In percutaneous intervention for treatment of coronary plaques, guidewire navigation is a primary procedure for stent delivery.
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
To overcome this problem, we introduce Description-based Embedding, DescEmb, a code-agnostic description-based representation learning framework for predictive modeling on EHR.
Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training
Recently a number of studies demonstrated impressive performance on diverse vision-language multi-modal tasks such as image captioning and visual question answering by extending the BERT architecture with multi-modal pre-training objectives.
T-Net: Nested encoder-decoder architecture for the main vessel segmentation in coronary angiography
As a result, all features from the low-level to the high-level extracted from the encoder are delivered from the beginning of the decoder to predict a more accurate mask.
Automated detection of vulnerable plaque in intravascular ultrasound images
The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule.
ECG arrhythmia classification using a 2-D convolutional neural network
In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification method using a deep two-dimensional convolutional neural network (CNN) which recently shows outstanding performance in the field of pattern recognition.
