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Found 22 papers, 7 papers with code
Contrastive Learning on Multimodal Analysis of Electronic Health Records
To accommodate the statistical analysis of multimodal EHR data, in this paper, we propose a novel multimodal feature embedding generative model and design a multimodal contrastive loss to obtain the multimodal EHR feature representation.
Inference of Dependency Knowledge Graph for Electronic Health Records
The effective analysis of high-dimensional Electronic Health Record (EHR) data, with substantial potential for healthcare research, presents notable methodological challenges.
Distributionally Robust Transfer Learning
Many existing transfer learning methods rely on leveraging information from source data that closely resembles the target data.
Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model
To overcome these challenges, we propose to infer the conditional dependency structure among EHR features via a latent graphical block model (LGBM).
LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records
We propose a LAbel-efficienT incidenT phEnotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data.
Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model
Network analysis has been a powerful tool to unveil relationships and interactions among a large number of objects.
Federated Offline Reinforcement Learning
Extensive simulations demonstrate the effectiveness of the proposed algorithm.
Multimodal Learning on Graphs for Disease Relation Extraction
On a dataset annotated by human experts, REMAP improves text-based disease relation extraction by 10. 0% (accuracy) and 17. 2% (F1-score) by fusing disease knowledge graphs with text information.
Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach
With only a small number of communications across participating sites, the proposed method can achieve performance comparable to the pooled analysis where individual-level data are directly pooled together.
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices
Besides, we prove the statistical rate for the eigenspace of the underlying matrix, which is comparable to the rate under the independently missing assumption.
Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction
Risk modeling with EHR data is challenging due to a lack of direct observations on the disease outcome, and the high dimensionality of the candidate predictors.
Semi-Supervised Off Policy Reinforcement Learning
2) The surrogate variables we leverage in the modified SSL framework are predictive of the outcome but not informative to the optimal policy or value function.
Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling
In step II, we augment the initial imputations to ensure the consistency of the resulting estimators regardless of the specification of the prediction model or the imputation model.
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
However, the adoption of such policies in practice is often challenging, as they are hard to interpret within the application context, and lack measures of uncertainty for the learned policy value and its decisions.
Multi-view Banded Spectral Clustering with Application to ICD9 Clustering
To bridge this gap, in this paper we propose a novel spectral clustering method that optimally combines multiple data sources while leveraging the prior distance knowledge.
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing.
A Fast Divide-and-Conquer Sparse Cox Regression
We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size $n_0$ is exceedingly large and the covariate dimension $p$ is not small but $n_0\gg p$.
Computation Applications
Surrogate Aided Unsupervised Recovery of Sparse Signals in Single Index Models for Binary Outcomes
We consider the recovery of regression coefficients, denoted by $\boldsymbol{\beta}_0$, for a single index model (SIM) relating a binary outcome $Y$ to a set of possibly high dimensional covariates $\boldsymbol{X}$, based on a large but 'unlabeled' dataset $\mathcal{U}$, with $Y$ never observed.
Efficient and Adaptive Linear Regression in Semi-Supervised Settings
It is often of interest to investigate if and when the unlabeled data can be exploited to improve estimation of the regression parameter in the adopted linear model.
L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
In the present paper we analyze algorithms based on covariance screening and least squares with $L_1$ penalization (i. e. LASSO) and demonstrate that they can also enjoy optimal (up to a scalar) rescaled sample size in terms of support recovery, albeit under slightly different assumptions on $f$ and $\varepsilon$ compared to the SIR based algorithms.
Structured Matrix Completion with Applications to Genomic Data Integration
Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering.
NILE: Fast Natural Language Processing for Electronic Health Records
This paper introduces the design and performance of Narrative Information Linear Extraction (NILE), a natural language processing (NLP) package for EHR analysis that we share with the medical informatics community.
