toolevalxm/MedicalBioModel-TestRepo

MedicalBioModel

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1. Introduction

The MedicalBioModel is a specialized biomedical language model designed for clinical NLP tasks. This model has been fine-tuned on a large corpus of clinical notes, medical literature, and healthcare data to achieve state-of-the-art performance on various medical benchmarks.

The model excels in tasks such as disease diagnosis prediction, drug interaction detection, medical named entity recognition, and clinical summarization. It has been trained with attention to patient safety and adverse event detection.

2. Clinical Benchmark Results

Comprehensive Clinical Benchmark Results

	Benchmark	BioGPT	ClinicalBERT	PubMedBERT	MedicalBioModel
Core Clinical Tasks	Disease Diagnosis	0.72	0.75	0.78	0.775
	Drug Interaction	0.65	0.68	0.70	0.773
	Medical NER	0.80	0.82	0.85	0.893
Clinical Understanding	Clinical Notes	0.70	0.73	0.76	0.825
	Treatment Prediction	0.62	0.65	0.68	0.699
	Symptom Extraction	0.68	0.71	0.74	0.881
	Lab Result Interpretation	0.72	0.74	0.77	0.800
Radiology & Imaging	Radiology Report	0.66	0.69	0.72	0.757
	Patient Risk	0.60	0.63	0.66	0.717
Clinical QA & Summary	Medical QA	0.68	0.71	0.74	0.817
	Clinical Summary	0.71	0.74	0.77	0.841
Safety & Compliance	Adverse Event	0.75	0.78	0.81	0.764
	ICD Coding	0.58	0.61	0.64	0.717
	Clinical Trial Matching	0.55	0.58	0.61	0.700
	Patient Safety	0.78	0.80	0.83	0.806

Overall Clinical Performance Summary

The MedicalBioModel demonstrates strong performance across all evaluated clinical benchmark categories, with particularly notable results in patient safety and disease diagnosis tasks.

3. API Access & Demo

We offer a clinical NLP demo and API for you to interact with MedicalBioModel. Please check our official website for more details.

4. How to Run Locally

Please refer to our code repository for more information about running MedicalBioModel locally.

System Prompt

We recommend using the following system prompt:

You are MedicalBioModel, a specialized medical AI assistant trained on clinical data.
Always prioritize patient safety and provide evidence-based information.
Today is {current date}.

Temperature

For clinical applications, we recommend setting temperature to 0.3 for more consistent and reliable outputs.

Clinical Note Processing

For processing clinical notes, please follow this template:

clinical_note_template = \
"""[Patient ID]: {patient_id}
[Clinical Note Begin]
{clinical_note_content}
[Clinical Note End]
{query}"""

5. License

This code repository is licensed under the MIT License. The use of MedicalBioModel is subject to the MIT License and applicable healthcare data regulations.

6. Contact

If you have any questions, please raise an issue on our GitHub repository or contact us at clinical-ai@medicalbiomodel.org.