Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Representation learning for knowledge fusion and reasoning in Cyber–Physical–Social Systems: Survey and perspectives
The digital deep integration of cyber space, physical space and social space facilitates the
formation of Cyber–Physical–Social Systems (CPSS). Knowledge empowers CPSS to be …
formation of Cyber–Physical–Social Systems (CPSS). Knowledge empowers CPSS to be …
MEGACare: Knowledge-guided multi-view hypergraph predictive framework for healthcare
Predicting a patient's future health condition by analyzing their Electronic Health Records
(EHRs) is a trending subject in the intelligent medical field, which can help clinicians …
(EHRs) is a trending subject in the intelligent medical field, which can help clinicians …
Cola-GNN: Cross-location attention based graph neural networks for long-term ILI prediction
Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-
care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease …
care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease …
Trust xai: Model-agnostic explanations for ai with a case study on iiot security
Despite artificial intelligence (AI)'s significant growth, its “black box” nature creates
challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in …
challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in …
Concare: Personalized clinical feature embedding via capturing the healthcare context
Predicting the patient's clinical outcome from the historical electronic medical records (EMR)
is a fundamental research problem in medical informatics. Most deep learning-based …
is a fundamental research problem in medical informatics. Most deep learning-based …
M3care: Learning with missing modalities in multimodal healthcare data
Multimodal electronic health record (EHR) data are widely used in clinical applications.
Conventional methods usually assume that each sample (patient) is associated with the …
Conventional methods usually assume that each sample (patient) is associated with the …
Attention-based multimodal fusion with contrast for robust clinical prediction in the face of missing modalities
Objective: With the increasing amount and growing variety of healthcare data, multimodal
machine learning supporting integrated modeling of structured and unstructured data is an …
machine learning supporting integrated modeling of structured and unstructured data is an …
[HTML][HTML] Hierarchical pretraining on multimodal electronic health records
Pretraining has proven to be a powerful technique in natural language processing (NLP),
exhibiting remarkable success in various NLP downstream tasks. However, in the medical …
exhibiting remarkable success in various NLP downstream tasks. However, in the medical …
Collaborative graph learning with auxiliary text for temporal event prediction in healthcare
Accurate and explainable health event predictions are becoming crucial for healthcare
providers to develop care plans for patients. The availability of electronic health records …
providers to develop care plans for patients. The availability of electronic health records …