Interpretable disease prediction based on reinforcement path reasoning over knowledge graphs
Objective: To combine medical knowledge and medical data to interpretably predict the risk
of disease. Methods: We formulated the disease prediction task as a random walk along a …
of disease. Methods: We formulated the disease prediction task as a random walk along a …
Predict and Interpret Health Risk Using Ehr Through Typical Patients
Z Yu, C Zhang, Y Wang, W Tang… - ICASSP 2024-2024 …, 2024 - ieeexplore.ieee.org
Predicting health risks from electronic health records (EHR) is a topic of recent interest.
Deep learning models have achieved success by modeling temporal and feature interaction …
Deep learning models have achieved success by modeling temporal and feature interaction …
Enhancing length of stay prediction by learning similarity-aware representations for hospitalized patients
T Zang, Y Zhu, X Huang, X Yang, Q Chen, J Yu… - Artificial Intelligence in …, 2023 - Elsevier
This paper focuses on predicting the length of stay for patients on the first day of admission
and propose a predictive model named DGLoS. In order to capture the influence of various …
and propose a predictive model named DGLoS. In order to capture the influence of various …
[HTML][HTML] Deep learning prediction models based on EHR trajectories: A systematic review
Abstract Background: Electronic health records (EHRs) are generated at an ever-increasing
rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients' …
rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients' …
Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks
Reduction in 30-day readmission rate is an important quality factor for hospitals as it can
reduce the overall cost of care and improve patient post-discharge outcomes. While deep …
reduce the overall cost of care and improve patient post-discharge outcomes. While deep …
Honest-GE: 2-step Heuristic Optimization and Node-level Embedding Empower Spatial-Temporal Graph Model for ECG
Graph-based models for Electrocardiogram (ECG) incorporate physiological spatial
information among ECG leads in elegant manners. It is unachievable for convolution-based …
information among ECG leads in elegant manners. It is unachievable for convolution-based …
Generative adversarial networks enhanced pre-training for insufficient electronic health records modeling
In recent years, automatic computational systems based on deep learning are widely used
in medical fields, such as automatic diagnosing and disease prediction. Most of these …
in medical fields, such as automatic diagnosing and disease prediction. Most of these …
Dl-bert: a time-aware double-level bert-style model with pre-training for disease prediction
X Chen, J Lin, Y An - … International Conference on Big Data (Big …, 2022 - ieeexplore.ieee.org
Disease prediction based on the Electronic Health Record (EHR) is an important task in
healthcare. EHR records patients' every visit by time, and there are many kinds of medical …
healthcare. EHR records patients' every visit by time, and there are many kinds of medical …
Medretriever: Target-driven interpretable health risk prediction via retrieving unstructured medical text
The broad adoption of electronic health record (EHR) systems and the advances of deep
learning technology have motivated the development of health risk prediction models, which …
learning technology have motivated the development of health risk prediction models, which …
Robustly extracting medical knowledge from EHRs: a case study of learning a health knowledge graph
Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically
learn medical knowledge. In one prominent example, a causal health knowledge graph …
learn medical knowledge. In one prominent example, a causal health knowledge graph …