GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data
Electronic health records (EHRs) are usually highly dimensional, heterogeneous, and multimodal. Besides, the random recording of clinical variables results in high missing rates and uneven time intervals between adjacent records in the multivariate clinical time-series data extracted from EHRs. Current works using clinical time-series data for patient representation regard the patients' physiological status as a discrete process described by sporadically collected records. However, changes in the patient's physiological condition are continuous and dynamic processes. The perception of time and velocity of change is crucial for patient representation learning. In this study, we propose a time- and velocity-aware gated recurrent unit model (GRU-TV) for patient representation learning of clinical multivariate time-series data in a time-continuous manner. The neural ordinary differential equations (ODEs) and velocity perception mechanism are applied to perceive the time interval between adjacent records and changing rate of the patient's physiological status, respectively. Our experiments on two real clinical EHR datasets (PhysioNet2012, MIMIC-III) establish that GRU-TV is a robust model on computer-aided diagnosis (CAD) tasks, especially on sequences with high-variance time intervals.
Code (0)
등록된 구현이 없습니다.
Tasks
Representation LearningTime SeriesTime Series AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Contrastive Representation Learning of Longitudinal Disease Trajectories on Temporal Graphs
Understanding disease trajectories from longitudinal clinical data remains challenging due to complex temporal dynamics and heterogeneous patient cohorts. Here, we present a contrastive representation learning framework …
Representation LearningContrastive LearningValue-aware transformers for 1.5d data
Sparse sequential highly-multivariate data of the form characteristic of hospital in-patient investigation and treatment poses a considerable challenge for representation learning. Such data is neither faithfully reducib…
FormLength-of-Stay predictionRepresentation LearningCollaborative learning of common latent representations in routinely collected multivariate ICU physiological signals
In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping. In contrast to previous research focused on electronic health rec…
Collaborative FilteringPatient PhenotypingTime SeriesVACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, labels are unavailable, and the cost of a miss is high. The central chall…
Self-Supervised Anomaly DetectionTime Series Anomaly DetectionSelf-Supervised LearningStatic and multivariate-temporal attentive fusion transformer for readmission risk prediction
Background: Accurate short-term readmission prediction of ICU patients is significant in improving the efficiency of resource assignment by assisting physicians in making discharge decisions. Clinically, both individual …
PredictionReadmission Prediction