# Neural Temporal Point Processes For Modelling Electronic Health Records

arXiv: https://arxiv.org/abs/2007.13794 · HTML: https://mlsanity.com/paper/2007.13794/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 30 of 39)

- [Neural Temporal Point Processes: A Review](https://mlsanity.com/paper/2104.03528/) (2021)
- [Transformer Embeddings of Irregularly Spaced Events and Their Participants](https://mlsanity.com/paper/2201.00044/) (2021)
- [HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences](https://mlsanity.com/paper/2210.01753/) (2022)
- [Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events](https://mlsanity.com/paper/2306.11547/) (2023)
- [Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning](https://mlsanity.com/paper/2305.16646/) (2023)
- [Anticipatory Music Transformer](https://mlsanity.com/paper/2306.08620/) (2023)
- [Add and Thin: Diffusion for Temporal Point Processes](https://mlsanity.com/paper/2311.01139/) (2023)
- [Cardinality-Regularized Hawkes-Granger Model](https://mlsanity.com/paper/2208.10671/) (2022)
- [On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data](https://mlsanity.com/paper/2306.17066/) (2023)
- [Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction](https://mlsanity.com/paper/2311.08635/) (2023)
- [An Empirical Study: Extensive Deep Temporal Point Process](https://mlsanity.com/paper/2110.09823/) (2021)
- [Edit-Based Flow Matching for Temporal Point Processes](https://mlsanity.com/paper/2510.06050/) (2025)
- [Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes](https://mlsanity.com/paper/2210.15294/) (2022)
- [Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes](https://mlsanity.com/paper/2401.04612/) (2024)
- [Detecting Anomalous Event Sequences with Temporal Point Processes](https://mlsanity.com/paper/2106.04465/) (2021)
- [Probabilistic Querying of Continuous-Time Event Sequences](https://mlsanity.com/paper/2211.08499/) (2022)
- [One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models](https://mlsanity.com/paper/2602.00541/) (2026)
- [TPP-Gaze: Modelling Gaze Dynamics in Space and Time with Neural Temporal Point Processes](https://mlsanity.com/paper/2410.23409/) (2024)
- [TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Records](https://mlsanity.com/paper/2402.06367/) (2024)
- [Federated Neural Nonparametric Point Processes](https://mlsanity.com/paper/2410.05637/) (2024)
- [Proactive Resource Request for Disaster Response: A Deep Learning-based Optimization Model](https://mlsanity.com/paper/2307.16661/) (2023)
- [Interpretable Neural Temporal Point Processes for Modelling Electronic Health Records](https://mlsanity.com/paper/2404.08007/) (2024)
- [On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes](https://mlsanity.com/paper/2406.00630/) (2024)
- [Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal Images](https://mlsanity.com/paper/2406.16077/) (2024)
- [Hawkes Process Based on Controlled Differential Equations](https://mlsanity.com/paper/2305.07031/) (2023)
- [Deep spatio-temporal point processes: Advances and new directions](https://mlsanity.com/paper/2504.06364/) (2025)
- [ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series](https://mlsanity.com/paper/2607.12391/) (2026)
- [Challenges and opportunities in applying Neural Temporal Point Processes to large scale industry data](https://mlsanity.com/paper/2208.08623/) (2022)
- [Intensity-free Integral-based Learning of Marked Temporal Point Processes](https://mlsanity.com/paper/2308.02360/) (2023)
- [On the Efficient Marginalization of Probabilistic Sequence Models](https://mlsanity.com/paper/2403.04005/) (2024)

## Abstract (arXiv)

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 are challenging to model due to their realisation as noisy, multi-modal data occurring at irregular time intervals. To address their temporal nature, we treat EHRs as samples generated by a Temporal Point Process (TPP), enabling us to model what happened in an event with when it happened in a principled way. We gather and propose neural network parameterisations of TPPs, collectively referred to as Neural TPPs. We perform evaluations on synthetic EHRs as well as on a set of established benchmarks. We show that TPPs significantly outperform their non-TPP counterparts on EHRs. We also show that an assumption of many Neural TPPs, that the class distribution is conditionally independent of time, reduces performance on EHRs. Finally, our proposed attention-based Neural TPP performs favourably compared to existing models, whilst aligning with real world interpretability requirements, an important step towards a component of clinical decision support systems.