8,709 research outputs found
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?
After being collected for patient care, Observational Health Data (OHD) can
further benefit patient well-being by sustaining the development of health
informatics and medical research. Vast potential is unexploited because of the
fiercely private nature of patient-related data and regulations to protect it.
Generative Adversarial Networks (GANs) have recently emerged as a
groundbreaking way to learn generative models that produce realistic synthetic
data. They have revolutionized practices in multiple domains such as
self-driving cars, fraud detection, digital twin simulations in industrial
sectors, and medical imaging.
The digital twin concept could readily apply to modelling and quantifying
disease progression. In addition, GANs posses many capabilities relevant to
common problems in healthcare: lack of data, class imbalance, rare diseases,
and preserving privacy. Unlocking open access to privacy-preserving OHD could
be transformative for scientific research. In the midst of COVID-19, the
healthcare system is facing unprecedented challenges, many of which of are data
related for the reasons stated above.
Considering these facts, publications concerning GAN applied to OHD seemed to
be severely lacking. To uncover the reasons for this slow adoption, we broadly
reviewed the published literature on the subject. Our findings show that the
properties of OHD were initially challenging for the existing GAN algorithms
(unlike medical imaging, for which state-of-the-art model were directly
transferable) and the evaluation synthetic data lacked clear metrics.
We find more publications on the subject than expected, starting slowly in
2017, and since then at an increasing rate. The difficulties of OHD remain, and
we discuss issues relating to evaluation, consistency, benchmarking, data
modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and
glossary, 51 in total. Inclusion of a large number of recent publications and
expansion of the discussion accordingl
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical
research and point of care applications; however, many challenges such as data
privacy concerns impede its optimal utilization. Deep generative models,
particularly, Generative Adversarial Networks (GANs) show great promise in
generating synthetic EHR data by learning underlying data distributions while
achieving excellent performance and addressing these challenges. This work aims
to review the major developments in various applications of GANs for EHRs and
provides an overview of the proposed methodologies. For this purpose, we
combine perspectives from healthcare applications and machine learning
techniques in terms of source datasets and the fidelity and privacy evaluation
of the generated synthetic datasets. We also compile a list of the metrics and
datasets used by the reviewed works, which can be utilized as benchmarks for
future research in the field. We conclude by discussing challenges in GANs for
EHRs development and proposing recommended practices. We hope that this work
motivates novel research development directions in the intersection of
healthcare and machine learning
A survey of generative adversarial networks for synthesizing structured electronic health records
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly, Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to survey the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning
Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting
modality for COVID-19 patients who are refractory to conventional therapies.
However, the proper treatment decision has been the subject of significant
debate and it remains controversial about who benefits from this scarcely
available and technically complex treatment option. To support clinical
decisions, it is a critical need to predict the treatment need and the
potential treatment and no-treatment responses. Targeting this clinical
challenge, we propose Treatment Variational AutoEncoder (TVAE), a novel
approach for individualized treatment analysis. TVAE is specifically designed
to address the modeling challenges like ECMO with strong treatment selection
bias and scarce treatment cases. TVAE conceptualizes the treatment decision as
a multi-scale problem. We model a patient's potential treatment assignment and
the factual and counterfactual outcomes as part of their intrinsic
characteristics that can be represented by a deep latent variable model. The
factual and counterfactual prediction errors are alleviated via a
reconstruction regularization scheme together with semi-supervision, and the
selection bias and the scarcity of treatment cases are mitigated by the
disentangled and distribution-matched latent space and the label-balancing
generative strategy. We evaluate TVAE on two real-world COVID-19 datasets: an
international dataset collected from 1651 hospitals across 63 countries, and a
institutional dataset collected from 15 hospitals. The results show that TVAE
outperforms state-of-the-art treatment effect models in predicting both the
propensity scores and factual outcomes on heterogeneous COVID-19 datasets.
Additional experiments also show TVAE outperforms the best existing models in
individual treatment effect estimation on the synthesized IHDP benchmark
dataset
Advancing Precision Medicine: Unveiling Disease Trajectories, Decoding Biomarkers, and Tailoring Individual Treatments
Chronic diseases are not only prevalent but also exert a considerable strain on the healthcare system, individuals, and communities. Nearly half of all Americans suffer from at least one chronic disease, which is still growing. The development of machine learning has brought new directions to chronic disease analysis. Many data scientists have devoted themselves to understanding how a disease progresses over time, which can lead to better patient management, identification of disease stages, and targeted interventions. However, due to the slow progression of chronic disease, symptoms are barely noticed until the disease is advanced, challenging early detection. Meanwhile, chronic diseases often have diverse underlying causes and can manifest differently among patients. Besides the external factors, the development of chronic disease is also influenced by internal signals. The DNA sequence-level differences have been proven responsible for constant predisposition to chronic diseases. Given these challenges, data must be analyzed at various scales, ranging from single nucleotide polymorphisms (SNPs) to individuals and populations, to better understand disease mechanisms and provide precision medicine. Therefore, this research aimed to develop an automated pipeline from building predictive models and estimating individual treatment effects based on the structured data of general electronic health records (EHRs) to identifying genetic variations (e.g., SNPs) associated with diseases to unravel the genetic underpinnings of chronic diseases. First, we used structured EHRs to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. In this step, we employed causal inference methods (constraint-based and functional causal models) for feature selection and utilized Markov chains, attention long short-term memory (LSTM), and Gaussian process (GP). SHapley Additive exPlanations (SHAPs) and local interpretable model-agnostic explanations (LIMEs) further extended the work to identify important clinical features. Next, I developed a novel counterfactual-based method to predict individual treatment effects (ITE) from observational data. To discern a “balanced” representation so that treated and control distributions look similar, we disentangled the doctor’s preference from the covariance and rebuilt the representation of the treated and control groups. We use integral probability metrics to measure distances between distributions. The expected ITE estimation error of a representation was the sum of the standard generalization error of that representation and the distance between the distributions induced. Finally, we performed genome-wide association studies (GWAS) based on the stage information we extracted from our unsupervised disease progression model to identify the biomarkers and explore the genetic correction between the disease and its phenotypes
A Perspective on Individualized Treatment Effects Estimation from Time-series Health Data
The burden of diseases is rising worldwide, with unequal treatment efficacy
for patient populations that are underrepresented in clinical trials.
Healthcare, however, is driven by the average population effect of medical
treatments and, therefore, operates in a "one-size-fits-all" approach, not
necessarily what best fits each patient. These facts suggest a pressing need
for methodologies to study individualized treatment effects (ITE) to drive
personalized treatment. Despite the increased interest in
machine-learning-driven ITE estimation models, the vast majority focus on
tabular data with limited review and understanding of methodologies proposed
for time-series electronic health records (EHRs). To this end, this work
provides an overview of ITE works for time-series data and insights into future
research. The work summarizes the latest work in the literature and reviews it
in light of theoretical assumptions, types of treatment settings, and
computational frameworks. Furthermore, this work discusses challenges and
future research directions for ITEs in a time-series setting. We hope this work
opens new directions and serves as a resource for understanding one of the
exciting yet under-studied research areas
Matching in Selective and Balanced Representation Space for Treatment Effects Estimation
The dramatically growing availability of observational data is being
witnessed in various domains of science and technology, which facilitates the
study of causal inference. However, estimating treatment effects from
observational data is faced with two major challenges, missing counterfactual
outcomes and treatment selection bias. Matching methods are among the most
widely used and fundamental approaches to estimating treatment effects, but
existing matching methods have poor performance when facing data with high
dimensional and complicated variables. We propose a feature selection
representation matching (FSRM) method based on deep representation learning and
matching, which maps the original covariate space into a selective, nonlinear,
and balanced representation space, and then conducts matching in the learned
representation space. FSRM adopts deep feature selection to minimize the
influence of irrelevant variables for estimating treatment effects and
incorporates a regularizer based on the Wasserstein distance to learn balanced
representations. We evaluate the performance of our FSRM method on three
datasets, and the results demonstrate superiority over the state-of-the-art
methods.Comment: Proceedings of the 29th ACM International Conference on Information
and Knowledge Management (CIKM '20
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