SNOWTEAM
/

sft_medico-mistral

@@ -53,7 +53,7 @@ Using open source instruction tuning datasets are composed of 4 main parts: (Som
    In patient-doctor conversations, patients often describe their symptoms in a colloquial and brief manner. When synthetic patient-doctor conversation datasets are manually created, they tend to lack diversity and become overly specialized, making them less reflective of real-life scenarios. A more effective approach is to collect real patient-doctor conversations. The \textit{HealthCareMagic-100k} dataset addresses this by gathering approximately 100,000 genuine doctor-patient interactions from online medical advice websites. These conversations were filtered manually and automatically to remove identifiers and corrected for grammatical errors using a language tool. Additionally, around 10,000 conversations from the online medical advice website iCliniq were collected and 5k conversations between patients and doctors were generated via ChatGPT by ChatDoctor for supplementation and evaluation (Yunxiang et al. 2023).
-4. **Multi-turn Medical Dialogue Data:**
    The only model currently trained using a multi-round dialog dataset is the Zhongjing-LLaMA model(Songhua et al. 2023). This model uses the CMtMedQA dataset, which is the first large-scale multi-round TCM QA dataset suitable for LLM training, and can significantly enhance the model's multi-round QA capability. However, this dataset collects data for online QA conversations, and lacks the ability to understand pathology examination, or image examination results, which has limitations in real clinical QA situations. Therefore, we used real electronic medical record EMRs obtained from hospitals, rewritten into multi-round conversations by prompting gpt.When designing the prompts, in order to standardize the questioning process and improve the differential diagnosis accuracy, we referred to the Mini-CEX, a clinical questioning assessment index used in medical schools, and the LLM-Mini-CEX, a new criterion that has been modified specifically for large language models (Xiaoming et al. 2023).
 ### Medical-Specific Instruction Tuning

    In patient-doctor conversations, patients often describe their symptoms in a colloquial and brief manner. When synthetic patient-doctor conversation datasets are manually created, they tend to lack diversity and become overly specialized, making them less reflective of real-life scenarios. A more effective approach is to collect real patient-doctor conversations. The \textit{HealthCareMagic-100k} dataset addresses this by gathering approximately 100,000 genuine doctor-patient interactions from online medical advice websites. These conversations were filtered manually and automatically to remove identifiers and corrected for grammatical errors using a language tool. Additionally, around 10,000 conversations from the online medical advice website iCliniq were collected and 5k conversations between patients and doctors were generated via ChatGPT by ChatDoctor for supplementation and evaluation (Yunxiang et al. 2023).
+4. **Multi-turn Medical Dialogue Data:(This data was not used to train this version of the model)**
    The only model currently trained using a multi-round dialog dataset is the Zhongjing-LLaMA model(Songhua et al. 2023). This model uses the CMtMedQA dataset, which is the first large-scale multi-round TCM QA dataset suitable for LLM training, and can significantly enhance the model's multi-round QA capability. However, this dataset collects data for online QA conversations, and lacks the ability to understand pathology examination, or image examination results, which has limitations in real clinical QA situations. Therefore, we used real electronic medical record EMRs obtained from hospitals, rewritten into multi-round conversations by prompting gpt.When designing the prompts, in order to standardize the questioning process and improve the differential diagnosis accuracy, we referred to the Mini-CEX, a clinical questioning assessment index used in medical schools, and the LLM-Mini-CEX, a new criterion that has been modified specifically for large language models (Xiaoming et al. 2023).
 ### Medical-Specific Instruction Tuning