Exploring Long-Term Prediction of Type 2 Diabetes Microvascular Complications

TL;DR

This study evaluates a code-agnostic, language model-based approach for long-term prediction of microvascular complications in Type 2 Diabetes patients using UK EHR data, showing improved performance over traditional code-based models.

Contribution

It introduces a novel text-based, code-agnostic modeling method for predicting diabetes complications, emphasizing the importance of context length in model performance.

Findings

Code-agnostic models outperform code-based models.

Prediction accuracy improves with longer time windows.

Performance is biased towards earlier complications.

Abstract

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.