Update README.md

README.md

@@ -25,10 +25,8 @@ Base model: Llama 3.1 8B (English) / Qwen 2.5 7B (Chinese)
 
 ### Model Description
 
-GENIE (Generative Note Information Extraction) is an end-to-end model for structuring EHR data, which is completed by cooperation between Sheng Yu's group (https://www.stat.tsinghua.edu.cn/teachers/shengyu/) and Tianxi Cai's group (https://dbmi.hms.harvard.edu/people/tianxi-cai).
-GENIE can process an entire paragraph of clinical notes in a single pass, outputting structured information on named entities, assertion statuses, locations, other relevant modifiers, clinical values, and intended purposes.
-This end-to-end approach simplifies the structuring process, reduces errors, and enables healthcare providers to derive structured data from EHRs more efficiently, without the need for extensive manual adjustments.
-And experiments have shown that GENIE achieves high accuracy in each of the task.
+GENIE (Generative Note Information Extraction) is an end-to-end model designed to structure free text from electronic health records (EHRs). It processes EHRs in a single pass, extracting biomedical named entities along with their assertion statuses, body locations, modifiers, values, units, and intended purposes, outputting this information in a structured JSON format. This streamlined approach simplifies traditional natural language processing workflows by replacing all the analysis components with a single model, making the system easier to maintain while leveraging the advanced analytical capabilities of large language models (LLMs). Comparing with general-purpose LLMs, GENIE does not require prompt engineering or few-shot examples. Additionally, it generates all relevant attributes in one pass, significantly reducing both runtime and operational costs.
+GENIE is codeveloped by Sheng Yu's group (https://www.stat.tsinghua.edu.cn/teachers/shengyu/) and Tianxi Cai's group (https://dbmi.hms.harvard.edu/people/tianxi-cai).
 
 
 ## Usage