Modeling Irregularly Sampled Clinical Time Series
While the volume of electronic health records (EHR) data continues to grow, it remains rare for hospital systems to capture dense physiological data streams, even in the data-rich intensive care unit setting. Instead, typical EHR records consist of sparse and irregularly observed multivariate time series, which are well understood to present particularly challenging problems for machine learning methods. In this paper, we present a new deep learning architecture for addressing this problem based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions during the interpolation stage, while any standard deep learning model can be used for the prediction network. We investigate the performance of this architecture on the problems of mortality and length of stay prediction.
Code (1)
Tasks
Deep LearningLength-of-Stay predictionPredictionTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Forecasting in multivariate irregularly sampled time series with missing values
Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains. Most recent approaches focus on classification, regression or forecasting tasks on such data. In …
General ClassificationIrregular Time SeriesMissing Valuesregression+2A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series
Irregularly sampled time series data arise naturally in many application domains including biology, ecology, climate science, astronomy, and health. Such data represent fundamental challenges to many classical models fro…
AstronomyBIG-bench Machine LearningSurveyTime Series+1Trajectory Flow Matching with Applications to Clinical Time Series Modeling
Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medicine. Neural stochastic differential equations (Neural SDEs) are an attractive mod…
Time SeriesTime Series as Images: Vision Transformer for Irregularly Sampled Time Series
Irregularly sampled time series are increasingly prevalent, particularly in medical domains. While various specialized methods have been developed to handle these irregularities, effectively modeling their complex dynami…
image-classificationImage ClassificationTime SeriesTime Series ClassificationLearning from Irregularly-Sampled Time Series: A Missing Data Perspective
Irregularly-sampled time series occur in many domains including healthcare. They can be challenging to model because they do not naturally yield a fixed-dimensional representation as required by many standard machine lea…
DecoderTime SeriesTime Series Analysis