Comparing Large Language Models for Improved Clinical Temporal Relation Extraction
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By
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Jianping He
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Zenan Sun
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Laila Rasmy
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Haifang Li
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Jianfu Li
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Evan Yu
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Degui Zhi
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Cui Tao
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October 15, 2026
Clinical Scorecard: Comparing Large Language Models for Improved Clinical Temporal Relation Extraction
At a Glance
| Category | Detail |
|---|
| Condition | Clinical temporal relation extraction (CTRE) |
| Key Mechanisms | Identifies temporal relationships among clinical events and information about patients’ past, present, and future statuses in clinical text. |
| Target Population | Clinical notes and electronic health record narratives; no patient cohort is specified in the provided article excerpt. |
| Care Setting | Clinical natural language processing and health-record research focused on reconstructing event timelines. |
Key Highlights
- CTRE supports chronological interpretation of events documented in clinical records.
- Clinical notes’ flexible formatting, atypical grammar, abbreviations, and acronyms make temporal relation annotation difficult.
- CTRE has lower interannotator agreement than clinical event and temporal mention annotation, complicating creation of large supervised datasets.
- Methods described include rule-based, machine learning, deep learning, and hybrid approaches.
Guideline-Based Recommendations
Diagnosis
Management
Monitoring & Follow-up
Risks
- Lower interannotator agreement complicates development of high-quality, large-scale supervised CTRE datasets.
- Flexible note formats and atypical grammar, abbreviations, and acronyms challenge reliable annotation.
Patient & Prescribing Data
No patient population or clinical treatment cohort is described in the provided excerpt.
Not applicable; the excerpt concerns clinical text-processing methods rather than patient treatment.
Clinical Best Practices
- Account for flexible formatting, atypical grammatical constructions, abbreviations, and acronyms when developing or evaluating CTRE systems.
Related Resources & Content
