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fabriceyhc/
Bio_ClinicalBERT-DrugDetector

Name: Bio_ClinicalBERT-DrugDetector
Availability: InStock

safetensors

bert

text-classification

multi-label-classification

clinical

forensic

overdose-surveillance

emilyalsentzer/Bio_ClinicalBERT

finetune

mit

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Bio_ClinicalBERT-DrugDetector

A multi-label classifier fine-tuned from emilyalsentzer/Bio_ClinicalBERT to detect substance involvement in death-certificate cause-of-death text, for overdose surveillance research (ROSLA pipeline).

Given free-text cause-of-death fields, it predicts 10 binary labels:

Methamphetamine, Heroin, Cocaine, Fentanyl, Alcohol, Prescription.opioids, Any Opioids, Benzodiazepines, Others, Any Drugs

Any Opioids and Any Drugs are aggregate flags (true if any opioid / any substance is implicated, even when the specific drug isn't independently confirmed elsewhere).

Performance

Macro F1 = 0.9811 on a held-out external test set (3,335 records, disjoint from training data), evaluated with per-label decision thresholds (not the default 0.5 — see best_thresholds.json).

Label	F1	Precision	Recall	Support
Methamphetamine	0.964	0.930	1.000	93
Heroin	0.987	0.975	1.000	78
Cocaine	0.995	0.990	1.000	381
Fentanyl	0.999	0.998	1.000	615
Alcohol	0.992	1.000	0.983	357
Prescription.opioids	0.992	0.992	0.992	129
Any Opioids	0.998	0.997	1.000	665
Benzodiazepines	0.995	0.991	1.000	105
Others	0.900	0.960	0.848	309
Any Drugs	0.988	0.997	0.979	967

⚠️ Required text preprocessing

This model was trained on normalized text, and needs the same normalization at inference time or performance will silently degrade. An earlier version of this model, evaluated without matching preprocessing, dropped from ~0.98 to ~0.78 macro F1 on the exact same test set — same model, same weights, just un-normalized input text.

Apply this function to your text before tokenizing:

python

import re

def clean_bert_text(value) -> str:
    if value is None or (isinstance(value, float) and value != value):  # NaN
        return ""
    s = str(value)
    s = s.replace("\t", " ").replace("\n", " ").replace("\r", " ")
    s = re.sub(r"\bNULL\b", " ", s, flags=re.IGNORECASE)
    s = s.replace(", ", " ")
    s = s.upper()
    return re.sub(r"\s+", " ", s).strip()

This strips literal "NULL" placeholder tokens, tabs/newlines, a "comma after every word" tokenization artifact present in some upstream data sources, and forces uppercase (the training data has no lowercase examples, so casing is not a signal the model can use — normalizing avoids an artificial train/inference mismatch).

Usage

python

import json
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

MODEL_ID = "fabriceyhc/Bio_ClinicalBERT-DrugDetector"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
model.eval()

# Per-label decision thresholds -- do NOT use the default 0.5, thresholds
# were individually tuned per label on a held-out validation split.
import huggingface_hub
thresholds_path = huggingface_hub.hf_hub_download(MODEL_ID, "best_thresholds.json")
thresholds = json.load(open(thresholds_path))

text = clean_bert_text("Acute intoxication due to the combined effects of fentanyl and cocaine")
inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True)
with torch.no_grad():
    probs = torch.sigmoid(model(**inputs).logits)[0]

for i, label in model.config.id2label.items():
    prob = probs[int(i)].item()
    pred = prob >= thresholds[label]
    print(f"{label:<25} prob={prob:.3f}  pred={pred}")

Training data

Fine-tuned on de-identified coroner/medical-examiner cause-of-death text (short phrases, e.g. "COMPLICATIONS OF FENTANYL AND COCAINE TOXICITY"), label-corrected via a consistency audit that checked for term/label agreement, logical-invariant violations between specific and aggregate columns, duplicate-text label conflicts, and negation. No names, addresses, or other directly identifying fields are present in the training text.

Base model: emilyalsentzer/Bio_ClinicalBERT (BERT-base architecture, 12 layers, 768 hidden, 12 heads)
Fine-tuning: 6 epochs, batch size 32, lr 2e-5, weight decay 0.01, early stopping (patience 2) on validation macro F1
Multi-label classification head (problem_type="multi_label_classification"), sigmoid + independent per-label thresholds rather than softmax

Limitations

Trained on a specific jurisdiction's death-certificate phrasing conventions; may not generalize to differently structured or differently worded cause-of-death text without re-validation.
Others (F1 0.900) is the weakest label — it's a catch-all category covering a long tail of less-common substances and has the least consistent training signal.
This is a research/surveillance tool for aggregate public-health monitoring, not a diagnostic or forensic determination instrument, and should not be used as the sole basis for any individual case decision.
Generic substance-class mentions (e.g. "opioid" without naming a specific drug) are intentionally routed only to the aggregate Any Opioids/Any Drugs flags, not to a specific-drug column, matching how the underlying data was labeled.