Hqg-net: Unpaired medical image enhancement with high-quality guidance
Unpaired medical image enhancement (UMIE) aims to transform a low-quality (LQ) medical
image into a high-quality (HQ) one without relying on paired images for training. While most …
image into a high-quality (HQ) one without relying on paired images for training. While most …
Metafed: Federated learning among federations with cyclic knowledge distillation for personalized healthcare
Federated learning (FL) has attracted increasing attention to building models without
accessing raw user data, especially in healthcare. In real applications, different federations …
accessing raw user data, especially in healthcare. In real applications, different federations …
[HTML][HTML] Heterogeneous network-based algorithms in the biomedical data mining: A review from technical perspective
S Yu, A Li, Y Chen, D Wang, X Tang - Informatics and Health, 2024 - Elsevier
Background Heterogeneous network-based methods are powerful analytical tools for many
real-world data mining tasks in biomedical field. The specific aim of this survey is to examine …
real-world data mining tasks in biomedical field. The specific aim of this survey is to examine …
TransLSTD: Augmenting hierarchical disease risk prediction model with time and context awareness via disease clustering
T You, Q Dang, Q Li, P Zhang, G Wu, W Huang - Information Systems, 2024 - Elsevier
The use of electronic health records has become widespread, providing a valuable source
of information for predicting disease risk. While deep neural network models have been …
of information for predicting disease risk. While deep neural network models have been …
SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients
P Yang, H Qiu, X Yang, L Wang, X Wang - Computer Methods and …, 2024 - Elsevier
Abstract Background and Objective: Colorectal cancer (CRC) is one of the most commonly
diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a …
diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a …
Multi-organ spatiotemporal information aware model for sepsis mortality prediction
X Feng, S Zhu, Y Shen, H Zhu, M Yan, G Cai… - Artificial Intelligence in …, 2024 - Elsevier
Background Sepsis is a syndrome involving multi-organ dysfunction, and the mortality in
sepsis patients correlates with the number of lesioned organs. Precise prognosis models …
sepsis patients correlates with the number of lesioned organs. Precise prognosis models …
Twin-RSA: deep learning-based automated heterogeneous data fusion approach for patient progression prediction using EHR data
Multiple electronic health records (EHRs) provide important opportunities to understand a
patient's statement of healthiness at various phases, particularly the improvement of a …
patient's statement of healthiness at various phases, particularly the improvement of a …
EAPR: explainable and augmented patient representation learning for disease prediction
Patient representation learning aims to encode meaningful information about the patient's
Electronic Health Records (EHR) in the form of a mathematical representation. Recent …
Electronic Health Records (EHR) in the form of a mathematical representation. Recent …
GAI and Deep Learning‐Based Medical Sensor Data Relationship Model for Health Informatics
Disease diagnosis is a hot research area in Health Informatics (Healthcare) record data
mining and an important step towards achieving generative intelligence (GAI) medical …
mining and an important step towards achieving generative intelligence (GAI) medical …
Integrated Local and Global Information for Health Risk Prediction Model
T You, Q Dang, Q Li - 2023 IEEE International Conference on …, 2023 - ieeexplore.ieee.org
Electronic health record (EHR) data has been widely used in health risk prediction models,
and it has an important preventive and intervention role in healthcare. Existing approaches …
and it has an important preventive and intervention role in healthcare. Existing approaches …