Add dataset files

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+language:
+  - en
+tags:
+  - healthcare
+  - medicine-quality
+  - pharmacovigilance
+  - ADR
+  - VigiBase
+  - ICSR
+  - drug-safety
+  - post-market
+  - sub-saharan-africa
+  - lmic
+pretty_name: "Post-Market Surveillance & Pharmacovigilance (ADR Reporting, Causality, VigiBase)"
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: functional_pv_centre
+    data_files: data/pv_functional_pv_centre.csv
+  - config_name: emerging_pv_system
+    data_files: data/pv_emerging_pv_system.csv
+    default: true
+  - config_name: minimal_pv_capacity
+    data_files: data/pv_minimal_pv_capacity.csv
+---
+
+# Post-Market Surveillance & Pharmacovigilance Dataset
+
+## Abstract
+
+This dataset provides **30,000 simulated pharmacovigilance reports** (10,000 per scenario) across three PV capacity levels in sub-Saharan Africa. Each record represents one adverse drug reaction report with 30+ variables including adverse event type, seriousness, causality assessment, SF medicine suspicion, ICSR completeness, VigiBase entry, and patient outcomes. Three scenarios: functional PV centre (25% serious, 65% causality assessed), emerging PV (35% serious, 25% assessed), minimal PV (50% serious, 5% assessed).
+
+**This dataset is entirely simulated. It must not be used for clinical or pharmacovigilance decisions.**
+
+## 1. Introduction
+
+Africa contributes <2% of global ICSR reports to VigiBase despite 17% of global population. Only 55% of African countries have functional PV centres. ADR under-reporting is estimated at 90-95% — only 5-10% of ADRs are captured. SF medicines drive a significant share of adverse events, particularly in settings with weak regulatory capacity.
+
+## 2. Parameterization Evidence (v2.0)
+
+| Parameter | Value Used | Source | Year |
+| --- | --- | --- | --- |
+| Africa <2% of VigiBase reports | <2% | UMC/VigiBase | 2023 |
+| 55% of African countries have PV | 55% | WHO | 2023 |
+| Under-reporting 90-95% | 90-95% | Lancet | 2019 |
+| ADR reporting 0.1-50/million | 0.1-50 | WHO | 2023 |
+
+## 3. Validation
+
+<p align="center">
+  <img src="validation_report.png" alt="Validation Report" width="100%">
+</p>
+
+## 4. Usage
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("electricsheepafrica/postmarket-surveillance-pharmacovigilance", "emerging_pv_system")
+df = dataset["train"].to_pandas()
+print(df.groupby('seriousness').size())
+```
+
+## 5. References
+
+1. Uppsala Monitoring Centre. VigiBase global ADR database.
+2. WHO (2023). Pharmacovigilance in Africa.
+3. Lancet (2019). Under-reporting of ADRs in SSA.
+4. USAID SIAPS/PQM+. PV system strengthening.
+
+## Citation
+
+```bibtex
+@dataset{esa_pv_2025,
+  title   = {Post-Market Surveillance and Pharmacovigilance Dataset},
+  author  = {{Electric Sheep Africa}},
+  year    = {2025},
+  publisher = {Hugging Face},
+  url     = {https://huggingface.co/datasets/electricsheepafrica/postmarket-surveillance-pharmacovigilance}
+}
+```
+
+## License
+
+[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)

generate_dataset.py

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+#!/usr/bin/env python3
+"""
+Literature-Informed Post-Market Surveillance & Pharmacovigilance Dataset
+=========================================================================
+
+Each record = ONE adverse drug reaction (ADR) or quality defect report.
+
+Sources (v2.0):
+[1] Uppsala Monitoring Centre (UMC). VigiBase — global ADR database.
+    Africa contributes <2% of global ICSR reports despite 17% of
+    global population and high SF medicine burden.
+[2] WHO (2023). Only 55% of African countries have functional PV
+    centres. ADR reporting rates: 0.1-50 per million population.
+[3] USAID SIAPS/PQM+. PV system strengthening in 15+ African
+    countries. Spontaneous reporting is the backbone.
+[4] WHO Global Individual Case Safety Report (ICSR) standards.
+    MedDRA coding for adverse events.
+[5] Lancet (2019). Under-reporting factor in SSA estimated at
+    90-95% — only 5-10% of ADRs are reported.
+"""
+
+import numpy as np
+import pandas as pd
+import argparse
+import os
+
+ADR_CATEGORIES = [
+    'hepatotoxicity', 'nephrotoxicity', 'skin_reaction_SJS',
+    'anaphylaxis', 'QT_prolongation', 'GI_disturbance',
+    'haematological_toxicity', 'neurotoxicity', 'ototoxicity',
+    'lactic_acidosis', 'tendon_rupture', 'hypoglycaemia',
+    'injection_site_reaction', 'drug_interaction', 'teratogenicity',
+]
+
+MEDICINE_CLASSES = [
+    'antimalarial', 'antibiotic', 'ARV', 'anti_TB',
+    'NSAID_analgesic', 'antihypertensive', 'antidiabetic',
+    'anticonvulsant', 'vaccine', 'herbal_traditional',
+    'antifungal', 'corticosteroid',
+]
+
+SCENARIOS = {
+    'functional_pv_centre': {
+        'pv_level': 'functional',
+        'reporting_rate_per_million': 40,
+        'ICSR_completeness': 0.70,
+        'causality_assessed': 0.65,
+        'sf_related_reports': 0.08,
+        'serious_proportion': 0.25,
+        'signal_detection_active': True,
+        'under_reporting_factor': 10,
+        'staff_pharmacovigilance': 8,
+    },
+    'emerging_pv_system': {
+        'pv_level': 'emerging',
+        'reporting_rate_per_million': 8,
+        'ICSR_completeness': 0.40,
+        'causality_assessed': 0.25,
+        'sf_related_reports': 0.15,
+        'serious_proportion': 0.35,
+        'signal_detection_active': False,
+        'under_reporting_factor': 25,
+        'staff_pharmacovigilance': 3,
+    },
+    'minimal_pv_capacity': {
+        'pv_level': 'minimal',
+        'reporting_rate_per_million': 0.5,
+        'ICSR_completeness': 0.15,
+        'causality_assessed': 0.05,
+        'sf_related_reports': 0.30,
+        'serious_proportion': 0.50,
+        'signal_detection_active': False,
+        'under_reporting_factor': 100,
+        'staff_pharmacovigilance': 0.5,
+    },
+}
+
+
+def generate_dataset(n=10000, seed=42, scenario='emerging_pv_system'):
+    rng = np.random.default_rng(seed)
+    sc = SCENARIOS[scenario]
+    records = []
+
+    for idx in range(n):
+        rec = {'id': idx + 1}
+        rec['pv_level'] = sc['pv_level']
+        rec['reporting_facility_id'] = f"PV_{rng.integers(1, 300):04d}"
+        rec['reporter_type'] = rng.choice(
+            ['physician', 'pharmacist', 'nurse', 'patient', 'CHW', 'other'],
+            p=[0.30, 0.25, 0.20, 0.10, 0.10, 0.05] if scenario == 'functional_pv_centre'
+            else ([0.20, 0.15, 0.25, 0.15, 0.15, 0.10] if scenario == 'emerging_pv_system'
+                  else [0.10, 0.05, 0.15, 0.30, 0.25, 0.15]))
+        rec['facility_type'] = rng.choice(
+            ['tertiary_hospital', 'district_hospital', 'health_centre',
+             'private_clinic', 'community', 'pharmacy'],
+            p=[0.25, 0.25, 0.20, 0.10, 0.10, 0.10] if scenario == 'functional_pv_centre'
+            else ([0.15, 0.20, 0.25, 0.15, 0.15, 0.10] if scenario == 'emerging_pv_system'
+                  else [0.05, 0.10, 0.15, 0.10, 0.40, 0.20]))
+
+        rec['patient_age_group'] = rng.choice(
+            ['neonate', 'infant', 'child', 'adolescent', 'adult', 'elderly'],
+            p=[0.03, 0.05, 0.10, 0.08, 0.55, 0.19])
+        rec['patient_sex'] = rng.choice(['male', 'female'], p=[0.45, 0.55])
+        rec['patient_pregnant'] = 1 if rec['patient_sex'] == 'female' and rng.random() < 0.08 else 0
+
+        rec['suspect_medicine_class'] = rng.choice(MEDICINE_CLASSES,
+            p=[0.12, 0.15, 0.12, 0.08, 0.10, 0.08, 0.07, 0.05,
+               0.08, 0.06, 0.05, 0.04])
+        rec['suspect_medicine_registered'] = 1 if rng.random() < (
+            0.90 if scenario == 'functional_pv_centre' else
+            (0.65 if scenario == 'emerging_pv_system' else 0.35)) else 0
+        rec['suspect_medicine_WHO_PQ'] = 1 if rng.random() < (
+            0.40 if scenario == 'functional_pv_centre' else
+            (0.15 if scenario == 'emerging_pv_system' else 0.03)) else 0
+
+        rec['sf_medicine_suspected'] = 1 if rng.random() < sc['sf_related_reports'] else 0
+        rec['sf_confirmed'] = 0
+        if rec['sf_medicine_suspected']:
+            rec['sf_confirmed'] = 1 if rng.random() < (
+                0.40 if scenario == 'functional_pv_centre' else
+                (0.15 if scenario == 'emerging_pv_system' else 0.03)) else 0
+
+        rec['adverse_event_type'] = rng.choice(ADR_CATEGORIES,
+            p=[0.08, 0.06, 0.07, 0.04, 0.03, 0.18, 0.06, 0.05, 0.03,
+               0.03, 0.02, 0.04, 0.08, 0.10, 0.13])
+        rec['seriousness'] = rng.choice(
+            ['non_serious', 'serious_hospitalisation', 'serious_disability',
+             'serious_life_threatening', 'fatal'],
+            p=[1-sc['serious_proportion'], sc['serious_proportion']*0.50,
+               sc['serious_proportion']*0.20, sc['serious_proportion']*0.20,
+               sc['serious_proportion']*0.10])
+
+        rec['causality_assessment'] = 'not_assessed'
+        if rng.random() < sc['causality_assessed']:
+            rec['causality_assessment'] = rng.choice(
+                ['certain', 'probable', 'possible', 'unlikely', 'unclassifiable'],
+                p=[0.05, 0.25, 0.40, 0.20, 0.10])
+
+        rec['ICSR_completeness_score'] = int(np.clip(
+            rng.normal(sc['ICSR_completeness'] * 100, 15), 5, 100))
+        rec['report_within_30_days'] = 1 if rng.random() < (
+            0.60 if scenario == 'functional_pv_centre' else
+            (0.25 if scenario == 'emerging_pv_system' else 0.05)) else 0
+        rec['follow_up_obtained'] = 1 if rng.random() < (
+            0.45 if scenario == 'functional_pv_centre' else
+            (0.15 if scenario == 'emerging_pv_system' else 0.03)) else 0
+        rec['entered_in_vigibase'] = 1 if rng.random() < (
+            0.70 if scenario == 'functional_pv_centre' else
+            (0.20 if scenario == 'emerging_pv_system' else 0.02)) else 0
+
+        rec['signal_detected'] = 0
+        if sc['signal_detection_active'] and rng.random() < 0.02:
+            rec['signal_detected'] = 1
+        rec['regulatory_action_triggered'] = 0
+        if rec['signal_detected'] or (rec['seriousness'] == 'fatal' and rng.random() < 0.15):
+            rec['regulatory_action_triggered'] = 1
+
+        rec['outcome'] = rng.choice(
+            ['recovered', 'recovering', 'not_recovered', 'fatal', 'unknown'],
+            p=[0.35, 0.20, 0.15, sc['serious_proportion']*0.10,
+               0.30 - sc['serious_proportion']*0.10])
+        rec['estimated_underreporting_factor'] = sc['under_reporting_factor']
+
+        rec['year'] = rng.choice([2020, 2021, 2022, 2023, 2024],
+                                 p=[0.10, 0.15, 0.20, 0.25, 0.30])
+
+        records.append(rec)
+
+    df = pd.DataFrame(records)
+    print(f"\n{'='*65}")
+    print(f"Pharmacovigilance — {scenario} (n={n}, seed={seed})")
+    print(f"{'='*65}")
+    print(f"  PV level: {sc['pv_level']}")
+    print(f"  Serious ADRs: {(df['seriousness']!='non_serious').mean()*100:.1f}%")
+    print(f"  SF-related: {df['sf_medicine_suspected'].mean()*100:.1f}%")
+    print(f"  Causality assessed: {(df['causality_assessment']!='not_assessed').mean()*100:.1f}%")
+    print(f"  VigiBase entry: {df['entered_in_vigibase'].mean()*100:.1f}%")
+    return df
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--all-scenarios', action='store_true')
+    parser.add_argument('--n', type=int, default=10000)
+    parser.add_argument('--seed', type=int, default=42)
+    args = parser.parse_args()
+    os.makedirs('data', exist_ok=True)
+    if args.all_scenarios:
+        for sc in SCENARIOS:
+            df = generate_dataset(n=args.n, seed=args.seed, scenario=sc)
+            df.to_csv(os.path.join('data', f'pv_{sc}.csv'), index=False)
+            print(f"  -> Saved\n")
+    else:
+        df = generate_dataset(n=args.n, seed=args.seed)
+        df.to_csv(os.path.join('data', 'pv_emerging_pv_system.csv'), index=False)

requirements.txt

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+numpy>=1.24
+pandas>=2.0
+matplotlib>=3.7

validate_dataset.py

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+#!/usr/bin/env python3
+"""Validation & Diagnostic Visualization for Post-Market Surveillance & Pharmacovigilance Dataset."""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+SCENARIOS = ['functional_pv_centre', 'emerging_pv_system', 'minimal_pv_capacity']
+
+
+def load_scenarios(data_dir='data'):
+    dfs = {}
+    for sc in SCENARIOS:
+        path = os.path.join(data_dir, f'pv_{sc}.csv')
+        if os.path.exists(path):
+            dfs[sc] = pd.read_csv(path)
+    return dfs
+
+
+def make_report(dfs, output='validation_report.png'):
+    fig, axes = plt.subplots(4, 2, figsize=(16, 24))
+    fig.suptitle(
+        'Post-Market Surveillance & Pharmacovigilance — Validation Report\n'
+        '(Functional PV → Emerging PV → Minimal PV)',
+        fontsize=15, fontweight='bold', y=0.99)
+    colors = ['#2ecc71', '#f39c12', '#e74c3c']
+    x = np.arange(len(SCENARIOS))
+    labels = ['Functional', 'Emerging', 'Minimal']
+
+    ax = axes[0, 0]
+    ser = [(dfs[sc]['seriousness']!='non_serious').mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, ser, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(ser):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Rate (%)'); ax.set_title('Serious ADR Proportion')
+
+    ax = axes[0, 1]
+    ca = [(dfs[sc]['causality_assessment']!='not_assessed').mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, ca, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(ca):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Rate (%)'); ax.set_title('Causality Assessment Done')
+
+    ax = axes[1, 0]
+    sf = [dfs[sc]['sf_medicine_suspected'].mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, sf, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(sf):
+        ax.text(i, v+0.5, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Rate (%)'); ax.set_title('SF Medicine Suspected in ADR Reports')
+
+    ax = axes[1, 1]
+    vb = [dfs[sc]['entered_in_vigibase'].mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, vb, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(vb):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Rate (%)'); ax.set_title('Entered in VigiBase')
+
+    ax = axes[2, 0]
+    df = dfs.get('emerging_pv_system', list(dfs.values())[1])
+    ae = df['adverse_event_type'].value_counts().head(10)
+    ax.barh(range(len(ae)), ae.values, color='#e74c3c', alpha=0.7)
+    ax.set_yticks(range(len(ae)))
+    ax.set_yticklabels([s.replace('_', ' ').title() for s in ae.index], fontsize=7)
+    ax.set_xlabel('Count'); ax.set_title('Top Adverse Event Types (Emerging)')
+
+    ax = axes[2, 1]
+    mc = df['suspect_medicine_class'].value_counts().head(10)
+    ax.barh(range(len(mc)), mc.values, color='#3498db', alpha=0.7)
+    ax.set_yticks(range(len(mc)))
+    ax.set_yticklabels([s.replace('_', ' ').title() for s in mc.index], fontsize=7)
+    ax.set_xlabel('Count'); ax.set_title('Top Suspect Medicine Classes (Emerging)')
+
+    ax = axes[3, 0]
+    sev = df['seriousness'].value_counts()
+    ax.pie(sev.values, labels=[s.replace('_', ' ').title() for s in sev.index],
+           autopct='%1.0f%%', colors=['#2ecc71', '#f39c12', '#e74c3c', '#9b59b6', '#34495e'][:len(sev)],
+           textprops={'fontsize': 7})
+    ax.set_title('Seriousness Distribution (Emerging)')
+
+    ax = axes[3, 1]
+    comp = [dfs[sc]['ICSR_completeness_score'].mean() for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, comp, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(comp):
+        ax.text(i, v+1, f'{v:.0f}', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Score (0-100)'); ax.set_title('ICSR Completeness Score')
+
+    plt.tight_layout(rect=[0, 0, 1, 0.97])
+    plt.savefig(output, dpi=150, bbox_inches='tight')
+    print(f'Saved validation report to {output}')
+    plt.close()
+
+
+if __name__ == '__main__':
+    dfs = load_scenarios()
+    if dfs:
+        make_report(dfs)