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

2025-05-16

AI Summary

This study addresses the challenge of structurally extracting date information from medical imaging text and accurately converting it into interpretable UNIX timestamps—a task poorly handled by existing methods. We propose a novel date parsing paradigm: first, reverse-engineering UNIX timestamps to construct many-to-one semantic mappings; then, leveraging these mappings to guide a regular expression synthesizer that learns deterministic, interpretable matching logic. Our approach integrates manual rule validation with automated synthesis, achieving high-precision date recognition on real-world medical text—significantly reducing false positives (particularly date-like noise) with only a marginal drop in recall. The key contribution is the first incorporation of UNIX time semantics into regex synthesis, yielding rules that are highly interpretable, modularly decomposable, and clinically actionable—thereby establishing a new pathway for standardizing temporal data in healthcare.

Technology Category

• Natural Language Processing: Information Extraction
• Computer Vision: Medical and Biological Imaging
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• Extracting explainable dates from medical images using reverse-engineered UNIX timestamps
• Improving date detection accuracy in text transcriptions via regular expression synthesis
• Reducing false positives in date identification while maintaining high detection rates

Innovation

• Reverse-engineering UNIX timestamps for date extraction
• Manually creating decomposable regular expressions
• Using regular expression synthesis to improve accuracy