用文本化EHR预测糖尿病微血管并发症，不依赖编码系统。
Exploring Long-Term Prediction of Type 2 Diabetes Microvascular Complications
- 将EHR转为文本，用预训练临床语言模型编码，实现无代码依赖预测。
- 10年窗口预测效果最佳，但模型更倾向首次发生并发症。
- 适合跨系统数据整合的临床研究者，尤其关注长期风险预测。
电子健康记录（EHR）包含大量可用于预测临床结局的数据。尽管常用ICD10、SNOMED等临床编码存储和分析，但不同注册系统与医疗机构间存在编码差异，跨系统整合需映射不同临床本体，常导致数据丢失。为此，已提出无代码依赖模型。本文评估了一种无代码表示方法在预测2型糖尿病患者长期微血管并发症中的有效性。通过微调预训练临床语言模型，将个体EHR编码为文本，并基于英国大规模EHR数据，采用多标签方法同时预测1年、5年和10年窗口内的并发症风险。结果表明，无代码方法优于基于编码的模型，且长期预测性能更佳，但模型对首次发生的并发症存在偏差。研究强调上下文长度对模型表现至关重要，证实了跨不同临床本体数据整合的可能性，为可泛化的临床模型提供了起点。
原文摘要 · Abstract (English)
Electronic healthcare records (EHR) contain a huge wealth of data that can support the prediction of clinical outcomes. EHR data is often stored and analysed using clinical codes (ICD10, SNOMED), however these can differ across registries and healthcare providers. Integrating data across systems involves mapping between different clinical ontologies requiring domain expertise, and at times resulting in data loss. To overcome this, code-agnostic models have been proposed. We assess the effectiveness of a code-agnostic representation approach on the task of long-term microvascular complication prediction for individuals living with Type 2 Diabetes. Our method encodes individual EHRs as text using fine-tuned, pretrained clinical language models. Leveraging large-scale EHR data from the UK, we employ a multi-label approach to simultaneously predict the risk of microvascular complications across 1-, 5-, and 10-year windows. We demonstrate that a code-agnostic approach outperforms a code-based model and illustrate that performance is better with longer prediction windows but is biased to the first occurring complication. Overall, we highlight that context length is vitally important for model performance. This study highlights the possibility of including data from across different clinical ontologies and is a starting point for generalisable clinical models.
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