332,962 research outputs found
Metric Learning for Temporal Sequence Alignment
In this paper, we propose to learn a Mahalanobis distance to perform
alignment of multivariate time series. The learning examples for this task are
time series for which the true alignment is known. We cast the alignment
problem as a structured prediction task, and propose realistic losses between
alignments for which the optimization is tractable. We provide experiments on
real data in the audio to audio context, where we show that the learning of a
similarity measure leads to improvements in the performance of the alignment
task. We also propose to use this metric learning framework to perform feature
selection and, from basic audio features, build a combination of these with
better performance for the alignment
Structured penalized regression for drug sensitivity prediction
Large-scale {\it in vitro} drug sensitivity screens are an important tool in
personalized oncology to predict the effectiveness of potential cancer drugs.
The prediction of the sensitivity of cancer cell lines to a panel of drugs is a
multivariate regression problem with high-dimensional heterogeneous multi-omics
data as input data and with potentially strong correlations between the outcome
variables which represent the sensitivity to the different drugs. We propose a
joint penalized regression approach with structured penalty terms which allow
us to utilize the correlation structure between drugs with group-lasso-type
penalties and at the same time address the heterogeneity between omics data
sources by introducing data-source-specific penalty factors to penalize
different data sources differently. By combining integrative penalty factors
(IPF) with tree-guided group lasso, we create the IPF-tree-lasso method. We
present a unified framework to transform more general IPF-type methods to the
original penalized method. Because the structured penalty terms have multiple
parameters, we demonstrate how the interval-search Efficient Parameter
Selection via Global Optimization (EPSGO) algorithm can be used to optimize
multiple penalty parameters efficiently. Simulation studies show that
IPF-tree-lasso can improve the prediction performance compared to other
lasso-type methods, in particular for heterogenous data sources. Finally, we
employ the new methods to analyse data from the Genomics of Drug Sensitivity in
Cancer project.Comment: Zhao Z, Zucknick M (2020). Structured penalized regression for drug
sensitivity prediction. Journal of the Royal Statistical Society, Series C.
19 pages, 6 figures and 2 table
Pattern-Based Analysis of Time Series: Estimation
While Internet of Things (IoT) devices and sensors create continuous streams
of information, Big Data infrastructures are deemed to handle the influx of
data in real-time. One type of such a continuous stream of information is time
series data. Due to the richness of information in time series and inadequacy
of summary statistics to encapsulate structures and patterns in such data,
development of new approaches to learn time series is of interest. In this
paper, we propose a novel method, called pattern tree, to learn patterns in the
times-series using a binary-structured tree. While a pattern tree can be used
for many purposes such as lossless compression, prediction and anomaly
detection, in this paper we focus on its application in time series estimation
and forecasting. In comparison to other methods, our proposed pattern tree
method improves the mean squared error of estimation
DuETT: Dual Event Time Transformer for Electronic Health Records
Electronic health records (EHRs) recorded in hospital settings typically
contain a wide range of numeric time series data that is characterized by high
sparsity and irregular observations. Effective modelling for such data must
exploit its time series nature, the semantic relationship between different
types of observations, and information in the sparsity structure of the data.
Self-supervised Transformers have shown outstanding performance in a variety of
structured tasks in NLP and computer vision. But multivariate time series data
contains structured relationships over two dimensions: time and recorded event
type, and straightforward applications of Transformers to time series data do
not leverage this distinct structure. The quadratic scaling of self-attention
layers can also significantly limit the input sequence length without
appropriate input engineering. We introduce the DuETT architecture, an
extension of Transformers designed to attend over both time and event type
dimensions, yielding robust representations from EHR data. DuETT uses an
aggregated input where sparse time series are transformed into a regular
sequence with fixed length; this lowers the computational complexity relative
to previous EHR Transformer models and, more importantly, enables the use of
larger and deeper neural networks. When trained with self-supervised prediction
tasks, that provide rich and informative signals for model pre-training, our
model outperforms state-of-the-art deep learning models on multiple downstream
tasks from the MIMIC-IV and PhysioNet-2012 EHR datasets.Comment: Accepted at MLHC 2023, camera-ready versio
Gaussian process prediction for time series of structured data
Paaßen B, Göpfert C, Hammer B. Gaussian process prediction for time series of structured data. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2016: 41--46.Time series prediction constitutes a classic topic in machine learning with wide-ranging applications, but mostly restricted to the domain of vectorial sequence entries. In recent years, time series of structured data (such as sequences, trees or graph structures) have become more and more important, for example in social network analysis or intelligent tutoring systems.
In this contribution, we propose an extension of time series models to strucured data based on Gaussian processes and structure kernels. We also provide speedup techniques for predictions in linear time, and we evaluate our approach on real data from the domain of intelligent tutoring systems
Detection and prediction of clopidogrel treatment failures using longitudinal structured electronic health records
We propose machine learning algorithms to automatically detect and predict
clopidogrel treatment failure using longitudinal structured electronic health
records (EHR). By drawing analogies between natural language and structured
EHR, we introduce various machine learning algorithms used in natural language
processing (NLP) applications to build models for treatment failure detection
and prediction. In this regard, we generated a cohort of patients with
clopidogrel prescriptions from UK Biobank and annotated if the patients had
treatment failure events within one year of the first clopidogrel prescription;
out of 502,527 patients, 1,824 patients were identified as treatment failure
cases, and 6,859 patients were considered as control cases. From the dataset,
we gathered diagnoses, prescriptions, and procedure records together per
patient and organized them into visits with the same date to build models. The
models were built for two different tasks, i.e., detection and prediction, and
the experimental results showed that time series models outperform bag-of-words
approaches in both tasks. In particular, a Transformer-based model, namely
BERT, could reach 0.928 AUC in detection tasks and 0.729 AUC in prediction
tasks. BERT also showed competence over other time series models when there is
not enough training data, because it leverages the pre-training procedure using
large unlabeled data
GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting
Transformer-based models have emerged as promising tools for time series
forecasting.
However, these model cannot make accurate prediction for long input time
series. On the one hand, they failed to capture global dependencies within time
series data. On the other hand, the long input sequence usually leads to large
model size and high time complexity.
To address these limitations, we present GCformer, which combines a
structured global convolutional branch for processing long input sequences with
a local Transformer-based branch for capturing short, recent signals. A
cohesive framework for a global convolution kernel has been introduced,
utilizing three distinct parameterization methods. The selected structured
convolutional kernel in the global branch has been specifically crafted with
sublinear complexity, thereby allowing for the efficient and effective
processing of lengthy and noisy input signals. Empirical studies on six
benchmark datasets demonstrate that GCformer outperforms state-of-the-art
methods, reducing MSE error in multivariate time series benchmarks by 4.38% and
model parameters by 61.92%. In particular, the global convolutional branch can
serve as a plug-in block to enhance the performance of other models, with an
average improvement of 31.93\%, including various recently published
Transformer-based models. Our code is publicly available at
https://github.com/zyj-111/GCformer
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