# Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference

arXiv: https://arxiv.org/abs/2012.08790 · HTML: https://mlsanity.com/paper/2012.08790/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 20 of 20)

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- [Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs](https://mlsanity.com/paper/2403.15273/) (2024)
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- [NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization](https://mlsanity.com/paper/2206.00906/) (2022)
- [Extraction of Medication and Temporal Relation from Clinical Text using Neural Language Models](https://mlsanity.com/paper/2310.02229/) (2023)
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- [TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems](https://mlsanity.com/paper/2406.14825/) (2024)
- [Transformer-Based Temporal Information Extraction and Application: A Review](https://mlsanity.com/paper/2504.07470/) (2025)
- [Building Patient Journeys in Hebrew: A Language Model for Clinical Timeline Extraction](https://mlsanity.com/paper/2512.11502/) (2025)
- [WISTERIA: Weak Implicit Signal-based Temporal Relation Extraction with Attention](https://mlsanity.com/paper/2603.23319/) (2026)
- [Beyond the Basics: Leveraging Large Language Model for Fine-Grained Medical Entity Recognition](https://mlsanity.com/paper/2604.17214/) (2026)

## Abstract (arXiv)

There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive feature engineering or are incapable of modeling the global relational dependencies among the events. In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level. Extensive experiments on two benchmark datasets, I2B2-2012 and TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods for temporal relation extraction.