---
language:
  - en
license: mit
tags:
  - pharmacovigilance
  - adverse-drug-reaction
  - biomedical-nlp
  - named-entity-recognition
  - text-classification
  - ghana
  - pubmedbert
  - dapt
datasets:
  - custom
metrics:
  - f1
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
model-index:
  - name: Ghana ADR Detection — CLF (Phase 6 / Phase 2b)
    results:
      - task:
          type: text-classification
          name: ADR Binary Classification
        metrics:
          - type: f1
            value: 0.724
            name: Macro F1 (LOSO)
  - name: Ghana ADR Detection — NER (Phase 7)
    results:
      - task:
          type: token-classification
          name: ADR Named Entity Recognition
        metrics:
          - type: f1
            value: 0.655
            name: Macro F1 (LOSO)
---

# Ghana ADR Detection System

Adverse drug reaction (ADR) detection from free-text clinical narratives, built on Ghanaian pharmacovigilance data. Fine-tuned from PubMedBERT with domain-adaptive pretraining (DAPT) on 128k Ghanaian biomedical sentences.


## Model Description

This repository contains two production heads trained on the same DAPT backbone:

| Component | Path | Task |
|---|---|---|
| DAPT backbone | `dapt-backbone/` | PubMedBERT MLM-adapted on Ghanaian biomedical corpus (PPL 6.11 → 4.55) |
| CLF head (Phase 2b) | `checkpoints/clf_phase2b_{fold}/clf_best/` | Binary: `contains_adr` 0/1 |
| NER head (Phase 7) | `checkpoints/ner_phase7_{fold}/ner_best/` | Token labels: `DRUG`, `ADR`, `SEVERITY`, `PATIENT_DEMO` |

**Production config (Phase 7 Hybrid):** `clf_phase2b_cohort_study` + `ner_phase7_cohort_study`, threshold 0.55.

## Performance

Evaluated with Leave-One-Source-Out (LOSO) cross-validation — each source domain is held out as the test set while the model trains on the remaining three. This measures real cross-domain generalisation across Ghanaian clinical writing styles.

### Classification (CLF)

| Held-out source | N | F1 |
|---|---|---|
| case_report | 44 | 0.787 |
| cohort_study | 123 | 0.776 |
| fda_newsletter | 99 | 0.667 |
| qualitative_interview | 78 | 0.667 |
| **macro-avg** | — | **0.724** |

### Named Entity Recognition (NER)

| Held-out source | N | F1 | DRUG F1 | ADR F1 |
|---|---|---|---|---|
| case_report | 44 | 0.598 | 0.862 | 0.545 |
| cohort_study | 123 | 0.785 | 0.823 | 0.884 |
| fda_newsletter | 99 | 0.587 | 0.626 | 0.634 |
| qualitative_interview | 78 | 0.650 | 0.560 | 0.842 |
| **macro-avg** | — | **0.655** | 0.718 | 0.727 |

Batch regression against 95 curated hard cases (Pidgin idioms, dialect, regulatory register, clinical shorthand, minimal pairs): **85/95 pass (89.5%)**.

## Training Data

Built from four Ghanaian pharmacovigilance source domains:

| Source | Type |
|---|---|
| Ghana FDA DrugLens newsletters (5 issues) | PDF — regulatory |
| Ghana FDA Annual Report 2023 + ADR Guide | PDF — regulatory |
| PMC open-access case reports & cohort studies (9 articles) | JATS XML — clinical |
| Patient ADR interview transcripts | Qualitative — community |

**Gold dataset:** 2,870 annotated sentences  
**Silver dataset:** 2,105+ records (DailyMed weak supervision, DrugLens NER, OpenFDA ICSR, synthetic curriculum)

## How to Use

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import AutoModelForTokenClassification
import torch

# Load DAPT backbone tokenizer
tokenizer = AutoTokenizer.from_pretrained("iamjamaal/ghana-adr-detection", subfolder="dapt-backbone")

# Load CLF head (production fold: cohort_study)
clf_model = AutoModelForSequenceClassification.from_pretrained(
    "iamjamaal/ghana-adr-detection",
    subfolder="checkpoints/clf_phase2b_cohort_study/clf_best"
)

# Load NER head (production fold: cohort_study)
ner_model = AutoModelForTokenClassification.from_pretrained(
    "iamjamaal/ghana-adr-detection",
    subfolder="checkpoints/ner_phase7_cohort_study/ner_best"
)

text = "Patient developed severe oculogyric crisis after starting haloperidol."

# CLF inference
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    logits = clf_model(**inputs).logits
prob_adr = torch.softmax(logits, dim=-1)[0][1].item()
contains_adr = prob_adr >= 0.55
print(f"ADR: {contains_adr} (p={prob_adr:.3f})")
```

## Repo Structure

```
dapt-backbone/                          # DAPT backbone (config + safetensors)
checkpoints/
  clf_phase2b_case_report/clf_best/     # CLF checkpoint — case_report fold
  clf_phase2b_cohort_study/clf_best/    # CLF checkpoint — cohort_study fold ← production
  clf_phase2b_fda_newsletter/clf_best/
  clf_phase2b_qualitative_interview/clf_best/
  ner_phase7_case_report/ner_best/      # NER checkpoint — case_report fold
  ner_phase7_cohort_study/ner_best/     # NER checkpoint — cohort_study fold ← production
  ner_phase7_fda_newsletter/ner_best/
  ner_phase7_qualitative_interview/ner_best/
  ner_phase7_qualitative_interview_seed/ner_best/
```

## Code & Demo

- **Pipeline code:** [github.com/iamjamaal/ghana-pharmacovigilance-ai](https://github.com/iamjamaal/ghana-pharmacovigilance-ai)
- **Live demo:** Flask app with single-sentence analysis, batch upload, and Yellow Card–style reporting

## Limitations

- Trained on Ghanaian pharmacovigilance sources; performance may degrade on clinical text from other regions.
- NER F1 on `SEVERITY` and `PATIENT_DEMO` is lower than `DRUG`/`ADR` due to limited annotation density.
- Ghanaian Pidgin and dialect constructions improve batch regression scores but may not generalise to other West African Pidgin variants.

## Citation

```bibtex
@misc{ghana-adr-2026,
  title  = {Ghana ADR Detection System},
  author = {Nabila, Noah Jamal},
  year   = {2026},
  url    = {https://huggingface.co/iamjamaal/ghana-adr-detection}
}
```

## License

Code: MIT | Model weights: MIT | Dataset annotations: CC-BY-4.0