Modeling Missing Data in Clinical Time Series with RNNs
We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the pediatric intensive care unit (PICU) at Children's Hospital Los Angeles, our data consists of multivariate time series of observations. The measurements are irregularly spaced, leading to missingness patterns in temporally discretized sequences. While these artifacts are typically handled by imputation, we achieve superior predictive performance by treating the artifacts as features. Unlike linear models, recurrent neural networks can realize this improvement using only simple binary indicators of missingness. For linear models, we show an alternative strategy to capture this signal. Training models on missingness patterns only, we show that for some diseases, what tests are run can be as predictive as the results themselves.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationImputationTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Missingness as Signal: Channel-Independent Spectrogram Learning for Clinical Time Series Prediction
Clinical time series prediction in intensive care units remains challenging due to heterogeneous physiological variables and informative missingness. The presence or absence of a measurement can reflect clinical decision…
Time Series PredictionMind the Missing: Variable-Aware Representation Learning for Irregular EHR Time Series using Large Language Models
Irregular sampling and high missingness are intrinsic challenges in modeling time series derived from electronic health records (EHRs),where clinical variables are measured at uneven intervals depending on workflow and i…
Representation LearningMedFuse: Multiplicative Embedding Fusion For Irregular Clinical Time Series
Clinical time series derived from electronic health records (EHRs) are inherently irregular, with asynchronous sampling, missing values, and heterogeneous feature dynamics. While numerical laboratory measurements are hig…
ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
Clinical time-series data are difficult to model with methods designed for regular sequences because they exhibit irregular sampling, frequent missing values, and heterogeneous observation patterns across variables. Exis…
Time Series PredictionBayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series
Real-world clinical time series data sets exhibit a high prevalence of missing values. Hence, there is an increasing interest in missing data imputation. Traditional statistical approaches impose constraints on the data-…
ImputationMissing ValuesMortality PredictionPrediction+2