OpenBioLLM-70B
O
Openbiollm 70B
Overview :
OpenBioLLM-70B is a sophisticated open-source language model developed by Saama AI Labs, designed specifically for the biomedical field. The model has been fine-tuned on a substantial amount of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. It demonstrates superior performance over other similarly-sized open-source biomedical language models in biomedical benchmark tests and outperforms larger proprietary and open-source models like GPT-4, Gemini, Medtron-70B, Med-PaLM-1, and Med-PaLM-2.
Target Users :
["Medical researchers: Assists in clinical decision support and medical research by providing accurate medical information and knowledge.","Developers: Can utilize this model to develop healthcare-related applications such as electronic health record analysis tools.","Healthcare professionals: Functions as an auxiliary tool to enhance the efficiency of processing and analyzing medical data for healthcare professionals."]
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Top Region: US(17.94%)
Website Views : 66.5K
Use Cases
Analyze and summarize clinical notes with OpenBioLLM-70B to enhance the management efficiency of medical records.
Utilize the model to answer medical questions such as neonatal jaundice, providing accurate medical information to parents.
Use the model for disease prediction and biomarker extraction in medical research.
Features
Clinical note summarization: Efficiently analyzes and summarizes complex clinical notes, electronic health records, and discharge summaries.
Answer medical questions: Provide answers to a wide range of medical questions.
Clinical entity recognition: Identifies and extracts key medical concepts such as diseases, symptoms, drugs, procedures, and anatomical structures from clinical text.
Biomarker extraction: Supports the extraction of biomarkers from biomedical texts.
Classification: Execute biomedical classification tasks such as disease prediction, sentiment analysis, and medical document classification.
De-identification: Detects and removes personal identifiable information from medical records to ensure patient privacy.
How to Use
Step 1: Visit the OpenBioLLM-Llama3-70B model page on Hugging Face.
Step 2: Invoke the model using the provided Transformers library code snippets, tailored to the specific task needed.
Step 3: Set appropriate model parameters, such as temperature (temperature) and top_p, to control the level of detail and diversity in the generated text.
Step 4: Input the user's questions or instructions into the model in the appropriate format to obtain the model's output.
Step 5: Analyze the model's output or proceed with subsequent analysis or application development based on the results.
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