23 research outputs found
Robust and Efficient Semi-supervised Learning for Ising Model
In biomedical studies, it is often desirable to characterize the interactive
mode of multiple disease outcomes beyond their marginal risk. Ising model is
one of the most popular choices serving for this purpose. Nevertheless,
learning efficiency of Ising models can be impeded by the scarcity of accurate
disease labels, which is a prominent problem in contemporary studies driven by
electronic health records (EHR). Semi-supervised learning (SSL) leverages the
large unlabeled sample with auxiliary EHR features to assist the learning with
labeled data only and is a potential solution to this issue. In this paper, we
develop a novel SSL method for efficient inference of Ising model. Our method
first models the outcomes against the auxiliary features, then uses it to
project the score function of the supervised estimator onto the EHR features,
and incorporates the unlabeled sample to augment the supervised estimator for
variance reduction without introducing bias. For the key step of conditional
modeling, we propose strategies that can effectively leverage the auxiliary EHR
information while maintaining moderate model complexity. In addition, we
introduce approaches including intrinsic efficient updates and ensemble, to
overcome the potential misspecification of the conditional model that may cause
efficiency loss. Our method is justified by asymptotic theory and shown to
outperform existing SSL methods through simulation studies. We also illustrate
its utility in a real example about several key phenotypes related to frequent
ICU admission on MIMIC-III data set
Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Biobank Studies
Surrogate variables in electronic health records (EHR) and biobank data play
an important role in biomedical studies due to the scarcity or absence of
chart-reviewed gold standard labels. We develop a novel approach named SASH for
{\bf S}urrogate-{\bf A}ssisted and data-{\bf S}hielding {\bf H}igh-dimensional
integrative regression. It is a semi-supervised approach that efficiently
leverages sizable unlabeled samples with error-prone EHR surrogate outcomes
from multiple local sites, to improve the learning accuracy of the small
gold-labeled data. {To facilitate stable and efficient knowledge extraction
from the surrogates, our method first obtains a preliminary supervised
estimator, and then uses it to assist training a regularized single index model
(SIM) for the surrogates. Interestingly, through a chain of convex and properly
penalized sparse regressions that approximate the SIM loss with
bias-correction, our method avoids the local minima issue of the SIM training,
and fully eliminates the impact of the preliminary estimator's large error. In
addition, it protects individual-level information through
summary-statistics-based data aggregation across the local sites, leveraging a
similar idea of bias-corrected approximation for SIM.} Through simulation
studies, we demonstrate that our method outperforms existing approaches on
finite samples. Finally, we apply our method to develop a high dimensional
genetic risk model for type II diabetes using large-scale data sets from UK and
Mass General Brigham biobanks, where only a small fraction of subjects in one
site has been labeled via chart reviewing
Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping
Electronic Health Record (EHR) data, a rich source for biomedical research, have been successfully used to gain novel insight into a wide range of diseases. Despite its potential, EHR is currently underutilized for discovery research due to its major limitation in the lack of precise phenotype information. To overcome such difficulties, recent efforts have been devoted to developing supervised algorithms to accurately predict phenotypes based on relatively small training datasets with gold-standard labels extracted via chart review. However, supervised methods typically require a sizable training set to yield generalizable algorithms, especially when the number of candidate features, p, is large. In this paper, we propose a semi-supervised (SS) EHR phenotyping method that borrows information from both a small, labeled dataset (where both the label Y and the feature set X are observed) and a much larger, weakly-labeled dataset in which the feature set X is accompanied only by a surrogate label S that is available to all patients. Under a working prior assumption that S is related to X only through Y and allowing it to hold approximately, we propose a prior adaptive semi-supervised (PASS) estimator that incorporates the prior knowledge by shrinking the estimator towards a direction derived under the prior. We derive asymptotic theory for the proposed estimator and justify its efficiency and robustness to prior information of poor quality. We also demonstrate its superiority over existing estimators under various scenarios via simulation studies and on three real-world EHR phenotyping studies at a large tertiary hospital
Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features
Due to label scarcity and covariate shift happening frequently in real-world
studies, transfer learning has become an essential technique to train models
generalizable to some target populations using existing labeled source data.
Most existing transfer learning research has been focused on model estimation,
while there is a paucity of literature on transfer inference for model accuracy
despite its importance. We propose a novel oubly obust
ugmented odel ccuracy ransfer
nferene (DRAMATIC) method for point and interval
estimation of commonly used classification performance measures in an unlabeled
target population using labeled source data. Specifically, DRAMATIC derives and
evaluates the risk model for a binary response against some low dimensional
predictors on the target population, leveraging from source
data only and high dimensional adjustment features from both the
source and target data. The proposed estimators are doubly robust in the sense
that they are consistent when at least one model is correctly
specified and certain model sparsity assumptions hold. Simulation results
demonstrate that the point estimation have negligible bias and the confidence
intervals derived by DRAMATIC attain satisfactory empirical coverage levels. We
further illustrate the utility of our method to transfer the genetic risk
prediction model and its accuracy evaluation for type II diabetes across two
patient cohorts in Mass General Brigham (MGB) collected using different
sampling mechanisms and at different time points
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Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program
One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations
Assessing Heterogeneous Risk of Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data
There have been increased concerns that the use of statins, one of the most
commonly prescribed drugs for treating coronary artery disease, is potentially
associated with the increased risk of new-onset type II diabetes (T2D).
However, because existing clinical studies with limited sample sizes often
suffer from selection bias issues, there is no robust evidence supporting as to
whether and what kind of populations are indeed vulnerable for developing T2D
after taking statins. In this case study, building on the biobank and
electronic health record data in the Partner Health System, we introduce a new
data analysis pipeline from a biological perspective and a novel statistical
methodology that address the limitations in existing studies to: (i)
systematically examine heterogeneous treatment effects of stain use on T2D
risk, (ii) uncover which patient subgroup is most vulnerable to T2D after
taking statins, and (iii) assess the replicability and statistical significance
of the most vulnerable subgroup via bootstrap calibration. Our proposed
bootstrap calibration approach delivers asymptotically sharp confidence
intervals and debiased estimates for the treatment effect of the most
vulnerable subgroup in the presence of possibly high-dimensional covariates. By
implementing our proposed approach, we find that females with high T2D genetic
risk at baseline are indeed at high risk of developing T2D due to statin use,
which provides evidences to support future clinical decisions with respect to
statin use.Comment: 31 pages, 2 figures, 6 table