509 research outputs found
Time series cluster kernels to exploit informative missingness and incomplete label information
The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture
models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing
values without resorting to imputation and the ensemble strategy ensures robustness to hyperparameters, making it particularly well suited for unsupervised learning.
However, TCK assumes missing at random and that the underlying missingness mechanism is ignorable, i.e. uninformative, an assumption that does not hold in many real-world applications, such as e.g.
medicine. To overcome this limitation, we present a kernel capable of exploiting the potentially rich information in the missing values and patterns, as well as the information from the observed data. In our
approach, we create a representation of the missing pattern, which is incorporated into mixed mode mixture models in such a way that the information provided by the missing patterns is effectively exploited.
Moreover, we also propose a semi-supervised kernel, capable of taking advantage of incomplete label
information to learn more accurate similarities.
Experiments on benchmark data, as well as a real-world case study of patients described by longitudinal
electronic health record data who potentially suffer from hospital-acquired infections, demonstrate the
effectiveness of the proposed method
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation
Traffic speed data imputation is a fundamental challenge for data-driven
transport analysis. In recent years, with the ubiquity of GPS-enabled devices
and the widespread use of crowdsourcing alternatives for the collection of
traffic data, transportation professionals increasingly look to such
user-generated data for many analysis, planning, and decision support
applications. However, due to the mechanics of the data collection process,
crowdsourced traffic data such as probe-vehicle data is highly prone to missing
observations, making accurate imputation crucial for the success of any
application that makes use of that type of data. In this article, we propose
the use of multi-output Gaussian processes (GPs) to model the complex spatial
and temporal patterns in crowdsourced traffic data. While the Bayesian
nonparametric formalism of GPs allows us to model observation uncertainty, the
multi-output extension based on convolution processes effectively enables us to
capture complex spatial dependencies between nearby road segments. Using 6
months of crowdsourced traffic speed data or "probe vehicle data" for several
locations in Copenhagen, the proposed approach is empirically shown to
significantly outperform popular state-of-the-art imputation methods.Comment: 10 pages, IEEE Transactions on Intelligent Transportation Systems,
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A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.
Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm’s pa- rameters and data-related modeling choices are also both crucial and challenging
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs
A large fraction of the electronic health records (EHRs) consists of clinical
measurements collected over time, such as lab tests and vital signs, which
provide important information about a patient's health status. These sequences
of clinical measurements are naturally represented as time series,
characterized by multiple variables and large amounts of missing data, which
complicate the analysis. In this work, we propose a novel kernel which is
capable of exploiting both the information from the observed values as well the
information hidden in the missing patterns in multivariate time series (MTS)
originating e.g. from EHRs. The kernel, called TCK, is designed using an
ensemble learning strategy in which the base models are novel mixed mode
Bayesian mixture models which can effectively exploit informative missingness
without having to resort to imputation methods. Moreover, the ensemble approach
ensures robustness to hyperparameters and therefore TCK is particularly
well suited if there is a lack of labels - a known challenge in medical
applications. Experiments on three real-world clinical datasets demonstrate the
effectiveness of the proposed kernel.Comment: 2020 International Workshop on Health Intelligence, AAAI-20. arXiv
admin note: text overlap with arXiv:1907.0525
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