EHRStruct: A Comprehensive Benchmark Framework for Evaluating Large Language Models on Structured Electronic Health Record Tasks

1. 1Define clinical tasks for large language models to perform on structured EHR data
2. 2Develop a standardized evaluation framework to assess model performance
3. 3Implement EHRStruct to compare and contrast the performance of different large language models
4. 4Use the results to inform model selection, fine-tuning, and development for improved clinical decision-making

Data scientists and AI engineers working in healthcare technology can benefit from EHRStruct to evaluate and improve the performance of large language models on clinical tasks, allowing them to make more informed decisions about model selection and development

💡 EHRStruct provides a standardized evaluation framework for assessing the performance of large language models on structured electronic health record tasks

📊 EHRStruct: a benchmark framework for evaluating LLMs on structured EHR tasks 🏥💻