Large ai models in health informatics: Applications, challenges, and the future
Large AI models, or foundation models, are models recently emerging with massive scales
both parameter-wise and data-wise, the magnitudes of which can reach beyond billions …
both parameter-wise and data-wise, the magnitudes of which can reach beyond billions …
The first step is the hardest: Pitfalls of representing and tokenizing temporal data for large language models
Large Language Models (LLMs) have demonstrated remarkable generalization across
diverse tasks, leading individuals to increasingly use them as personal assistants and …
diverse tasks, leading individuals to increasingly use them as personal assistants and …
Continuous patient state attention model for addressing irregularity in electronic health records
Background Irregular time series (ITS) are common in healthcare as patient data is recorded
in an electronic health record (EHR) system as per clinical guidelines/requirements but not …
in an electronic health record (EHR) system as per clinical guidelines/requirements but not …
A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification
The heart is a very crucial organ of the body. Concerted efforts are constantly put forward to
provide adequate monitoring of the heart. A heart disorder is reported to cause a lot of …
provide adequate monitoring of the heart. A heart disorder is reported to cause a lot of …
MARS: Multiagent Reinforcement Learning for Spatial–Spectral and Temporal Feature Selection in EEG-Based BCI
In recent years, deep learning methods have shown promising capabilities for extracting
informative and discriminative features from electroencephalography (EEG) data. However …
informative and discriminative features from electroencephalography (EEG) data. However …
RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG
Introduction: Deep learning models for detecting episodes of atrial fibrillation (AF) using
rhythm information in long-term ambulatory ECG recordings have shown high performance …
rhythm information in long-term ambulatory ECG recordings have shown high performance …
The Explainability of Transformers: Current Status and Directions
P Fantozzi, M Naldi - Computers, 2024 - mdpi.com
An increasing demand for model explainability has accompanied the widespread adoption
of transformers in various fields of applications. In this paper, we conduct a survey of the …
of transformers in various fields of applications. In this paper, we conduct a survey of the …
Quantitative Prediction of Right Ventricular Size and Function From the ECG
Background Right ventricular ejection fraction (RVEF) and end‐diastolic volume (RVEDV)
are not readily assessed through traditional modalities. Deep learning–enabled ECG …
are not readily assessed through traditional modalities. Deep learning–enabled ECG …
Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus …
Glaucoma, a disease that damages the optic nerve, is the leading cause of irreversible
blindness worldwide. The early detection of glaucoma is a challenge, which in recent years …
blindness worldwide. The early detection of glaucoma is a challenge, which in recent years …
Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs
EE Hwang, D Chen, Y Han, L Jia, J Shan - Bioengineering, 2023 - mdpi.com
Glaucomatous optic neuropathy (GON) can be diagnosed and monitored using fundus
photography, a widely available and low-cost approach already adopted for automated …
photography, a widely available and low-cost approach already adopted for automated …