Interpretable Neural Temporal Point Processes for Modelling Electronic Health Records
Electronic Health Records (EHR) can be represented as temporal sequences that record the events (medical visits) from patients. Neural temporal point process (NTPP) has achieved great success in modeling event sequences that occur in continuous time space. However, due to the black-box nature of neural networks, existing NTPP models fall short in explaining the dependencies between different event types. In this paper, inspired by word2vec and Hawkes process, we propose an interpretable framework inf2vec for event sequence modelling, where the event influences are directly parameterized and can be learned end-to-end. In the experiment, we demonstrate the superiority of our model on event prediction as well as type-type influences learning.
Code (0)
등록된 구현이 없습니다.
Tasks
Point ProcessesTemporal SequencesSimilar Papers 제목 키워드 기반
Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction
Predicting (1) when the next hospital admission occurs and (2) what will happen in the next admission about a patient by mining electronic health record (EHR) data can provide granular readmission predictions to assist c…
Decision MakingPoint ProcessesReadmission PredictionNeural Temporal Point Processes For Modelling Electronic Health Records
The modelling of Electronic Health Records (EHRs) has the potential to drive more efficient allocation of healthcare resources, enabling early intervention strategies and advancing personalised healthcare. However, EHRs …
Point ProcessesExplaining Point Processes by Learning Interpretable Temporal Logic Rules
We propose a principled method to learn a set of human-readable logic rules to explain temporal point processes. We assume that the generative mechanisms underlying the temporal point processes are governed by a set of …
Point ProcessesLearning mixture of neural temporal point processes for event sequence clustering
Event sequence clustering applies to many scenarios e.g. e-Commerce and electronic health. Traditional clustering models fail to characterize complex real-world processes due to the strong parametric assumption. While Ne…
ClusteringPoint ProcessesLatent Factor Point Processes for Patient Representation in Electronic Health Records
Electronic health records (EHR) contain valuable longitudinal patient-level information, yet most statistical methods reduce the irregular timing of EHR codes into simple counts, thereby discarding rich temporal structur…
Point Processes