Search Results for author:
Found 21 papers, 10 papers with code
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
The calibration of DeepSurv (IBS: 0. 041) performed the best, followed by RSF (IBS: 0. 042) and GBM (IBS: 0. 0421), all using the full variables.
MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
More importantly, in pseudo mapping learning, we propose a bi-directional voting (BDV) strategy that fuses the alignment decisions in different directions to estimate the uncertainty via the joint matching confidence score.
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
To address this, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured variables and some other measured variables.
Improving Knowledge Graph Entity Alignment with Graph Augmentation
Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion.
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice.
FedScore: A privacy-preserving framework for federated scoring system development
We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models.
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
We found that model backbone(s) differed among data types as well as the imputation strategy.
Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning
In this work, we propose a stance detection approach that can efficiently adapt to unseen targets, the core of which is to capture target-invariant syntactic expression patterns as transferable knowledge.
Latent Hierarchical Causal Structure Discovery with Rank Constraints
Most causal discovery procedures assume that there are no latent confounders in the system, which is often violated in real-world problems.
Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies
The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide.
EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting
Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health.
Inter- and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting
In Inter-Series Embedding Module, a multi-scale unified convolution component called Region-Aware Convolution is proposed, which cooperates with self-attention to capture dynamic dependencies between time series obtained from multiple regions.
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study
Risk scores are widely used for clinical decision making and commonly generated from logistic regression models.
Benchmarking emergency department triage prediction models with machine learning and large public electronic health records
In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400, 000 ED visits from 2011 to 2019.
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
To some extent, current deep learning solutions can address these challenges.
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i. e., mean value of sensitivity and specificity).
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i. e., Cox) and the random survival forest.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
Despite its success in certain domains, most existing methods focus on causal relations between observed variables, while in many scenarios the observed ones may not be the underlying causal variables (e. g., image pixels), but are generated by latent causal variables or confounders that are causally related.
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for Latent Factors (MD-LiNA), where the causal structure among latent factors of interest is shared for all domains, and we provide its identification results.
Triad Constraints for Learning Causal Structure of Latent Variables
In this paper, by properly leveraging the non-Gaussianity of the data, we propose to estimate the structure over latent variables with the so-called Triad constraints: we design a form of "pseudo-residual" from three variables, and show that when causal relations are linear and noise terms are non-Gaussian, the causal direction between the latent variables for the three observed variables is identifiable by checking a certain kind of independence relationship.
Research on the pixel-based and object-oriented methods of urban feature extraction with GF-2 remote-sensing images
During the rapid urbanization construction of China, acquisition of urban geographic information and timely data updating are important and fundamental tasks for the refined management of cities.
