# Extracting Explainable Dates From Medical Images By Reverse-Engineering UNIX Timestamps

arXiv: https://arxiv.org/abs/2505.11451 · HTML: https://mlsanity.com/paper/2505.11451/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 13 of 13)

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- [AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers](https://mlsanity.com/paper/2402.05602/) (2024)
- [Demystifying the Draft EU Artificial Intelligence Act](https://mlsanity.com/paper/2107.03721/) (2021)
- [Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)](https://mlsanity.com/paper/2001.00139/) (2020)
- [Learning Interpretable Temporal Properties from Positive Examples Only](https://mlsanity.com/paper/2209.02650/) (2022)
- [Data Extraction via Semantic Regular Expression Synthesis](https://mlsanity.com/paper/2305.10401/) (2023)
- [FOREST: An Interactive Multi-tree Synthesizer for Regular Expressions](https://mlsanity.com/paper/2012.14235/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fast and Accurate Contextual Knowledge Extraction Using Cascading Language Model Chains and Candidate Answers](https://mlsanity.com/paper/2507.22921/) (2025)

## Abstract (arXiv)

Dates often contribute towards highly impactful medical decisions, but it is rarely clear how to extract this data. AI has only just begun to be used transcribe such documents, and common methods are either to trust that the output produced by a complex AI model, or to parse the text using regular expressions. Recent work has established that regular expressions are an explainable form of logic, but it is difficult to decompose these into the component parts that are required to construct precise UNIX timestamps. First, we test publicly-available regular expressions, and we found that these were unable to capture a significant number of our dates. Next, we manually created easily-decomposable regular expressions, and we found that these were able to detect the majority of real dates, but also a lot of sequences of text that look like dates. Finally, we used regular expression synthesis to automatically identify regular expressions from the reverse-engineered UNIX timestamps that we created. We find that regular expressions created by regular expression synthesis detect far fewer sequences of text that look like dates than those that were manually created, at the cost of a slight increase to the number of missed dates. Overall, our results show that regular expressions can be created through regular expression synthesis to identify complex dates and date ranges in text transcriptions. To our knowledge, our proposed way of learning deterministic logic by reverse-engineering several many-one mappings and feeding these into a regular expression synthesiser is a new approach.