drugdetector 0.3.1

A simple wrapper to support drug detection in medical texts.

drugdetector

This project provides a simple wrapper for performant zero-shot drug detection using LLMs.

Install

pip install drugdetector

Optionally, but recommended is to install CUDA support for llama-cpp-python (source).

pip install llama-cpp-python \
  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/<cuda-version>

Where <cuda-version> is one of the following:

cu121: CUDA 12.1 cu122: CUDA 12.2 cu123: CUDA 12.3 cu124: CUDA 12.4 cu125: CUDA 12.5

You can find what version of CUDA you support via nvidia-smi and looking for the CUDA Version in the top right corner.

Demo

For this demo, we're using our fine-tuned 8-bit quantized Llama-3 model (fabriceyhc/Llama-3-8B-DrugDetector), but you can use many LLMs available on huggingface. We recommend one of the following fine-tuned models for improved performance:

• fabriceyhc/Llama-3-8B-DrugDetector
• fabriceyhc/Llama-3-70B-DrugDetector

from drugdetector import DrugDetector

detector = DrugDetector(model_id="fabriceyhc/Llama-3-8B-DrugDetector")

Detection on default substances

When no specific drugs are requested, default to the ones we've studied.

results = detector.detect("Patient denies using heroin but reports cocaine use.")
print(results)

{'Heroin': False,
 'Cocaine': True,
 'Methamphetamine': False,
 'Benzodiazepine': False,
 'Prescription Opioids': False,
 'Cannabis': False,
 'Injection Drugs': False,
 'General Drugs': True,
 'medical_text': 'Patient denies using heroin but reports cocaine use.',
 'time_taken': 9.533252477645874}

Detection with custom drug dict

Tests zero-shot accuracy on any substances provided in as a python dictionary in the form of:

new_substances = {
  "substance1":"description1",
  "substance2":"description2",
  ...
  "substanceN":"descriptionN",
}

new_substances = {
    "Alcohol": "Alcohol is a legal substance but can be abused and lead to addiction and various health issues.",
    "Fentanyl": "Fentanyl is a potent synthetic opioid that is highly addictive and can lead to overdose, especially when used illicitly."
}

results = detector.detect("Patient hx of ethl use.", drugs=new_substances)
print(results)

{'Alcohol': True,
 'Fentanyl': False,
 'medical_text': 'Patient hx of ethl use.',
 'time_taken': 2.379117965698242}

Detection with custom drug dict and explanations provided

Demonstrates the model's ability to explain its decisions.

new_drugs = {
    "Alcohol": "Alcohol is a legal substance but can be abused and lead to addiction and various health issues.",
    "Fentanyl": "Fentanyl is a potent synthetic opioid that is highly addictive and can lead to overdose, especially when used illicitly."
}

results = detector.detect("Patient hx of ethl use.", drugs=new_substances, explain=True)
print(results)

{'Alcohol': True,
 'Fentanyl': False,
 'medical_text': 'Patient hx of ethl use.',
 'time_taken': 45.255696296691895,
 'Alcohol_explanation': 'The medical note mentions "ethl use", which is likely referring to ethanol, the scientific name for alcohol.',
 'Fentanyl_explanation': 'There is no mention of Fentanyl use in the medical note.'}