Initial release: Synthetic TB Screening v1.0

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+language:
+  - en
+tags:
+  - synthetic
+  - healthcare
+  - tuberculosis
+  - tb
+  - genexpert
+  - smear-microscopy
+  - chest-xray
+  - hiv-tb
+  - mdr-tb
+  - who-guidelines
+  - lmic
+pretty_name: "Synthetic TB Screening & Symptom Dataset (GeneXpert, Smear, CXR)"
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: low_tb_burden
+    data_files: data/tb_low_tb_burden.csv
+  - config_name: moderate_tb_burden
+    data_files: data/tb_moderate_tb_burden.csv
+    default: true
+  - config_name: high_tb_burden
+    data_files: data/tb_high_tb_burden.csv
+---
+
+# Synthetic TB Screening & Symptom Dataset (GeneXpert, Smear, CXR)
+
+## Abstract
+
+This dataset provides **30,000 synthetic records** (10,000 per scenario) of patients undergoing TB screening at LMIC health facilities. Each record contains 25 variables including demographics, HIV status, BMI, 8 symptom indicators, WHO symptom screen score, diagnostic results (smear microscopy, GeneXpert MTB/RIF with rifampicin resistance, chest X-ray), TB classification, and treatment outcome. Diagnostic test performance (sensitivity, specificity) is modelled from Cochrane reviews and WHO product evaluations. Three burden scenarios (low, moderate, high) span TB prevalence from 6% to 35% among screened patients, with HIV co-infection rates from 10% to 46%.
+
+**This dataset is entirely synthetic. It must not be used for clinical decision-making.**
+
+## 2. Methodology
+
+### 2.1 Diagnostic Test Performance
+
+| Test | Sensitivity | Specificity | Source |
+| --- | --- | --- | --- |
+| Smear microscopy | 60% (40% if HIV+) | 98% | Getahun et al., Lancet ID 2007 |
+| GeneXpert (smear+) | 98% | 98% | Steingart et al., Cochrane 2014 |
+| GeneXpert (smear-) | 68% (79% HIV+) | 98% | Steingart et al., 2014 |
+| GeneXpert RIF resistance | 95% sensitivity | 98% specificity | WHO 2021 |
+| Chest X-ray | 87% | 70% | van't Hoog et al., PLoS One 2012 |
+
+### 2.2 Scenario Design
+
+| Scenario | TB Prevalence | HIV+ | TB-HIV Co-infection | MDR-TB | Smear+ |
+| --- | --- | --- | --- | --- | --- |
+| Low burden | 6.4% | 4.1% | 10% of TB cases | 3.1% | 5.4% |
+| Moderate burden | 17.2% | 10.8% | 26% of TB cases | 4.8% | 10.4% |
+| High burden | 35.1% | 27.5% | 46% of TB cases | 8.1% | 16.8% |
+
+## 3. Schema
+
+| Column | Type | Description |
+| --- | --- | --- |
+| age_years | int | Age (15-85) |
+| sex | categorical | M/F (M:F ratio ~1.4:1 per WHO) |
+| bmi | float | Body mass index |
+| hiv_status | binary | HIV serostatus |
+| cough_2weeks, fever, night_sweats, weight_loss | binary | WHO symptom screen |
+| hemoptysis, chest_pain, fatigue, loss_of_appetite | binary | Additional symptoms |
+| cough_duration_weeks | int | Duration of cough |
+| who_symptom_screen_count | int (0-4) | Number of WHO screening symptoms |
+| smear_result | binary | Sputum smear microscopy |
+| xpert_mtb_detected | binary | GeneXpert MTB detected |
+| xpert_rif_resistance | categorical | detected / not_detected / N/A |
+| cxr_result | categorical | normal / infiltrate / cavity / miliary / pleural_effusion / old_tb_scar / other_pathology |
+| cxr_abnormal | binary | Any CXR abnormality |
+| true_tb_status | binary | Ground truth TB status |
+| tb_type | categorical | none / pulmonary / extrapulmonary |
+| tb_classification | categorical | not_tb / smear_positive_ptb / smear_negative_ptb / extrapulmonary_tb |
+| rifampicin_resistant | binary | Drug resistance status |
+| treatment_outcome | categorical | cured / completed / failed / died / lost_to_followup / not_applicable |
+
+## 4. Validation
+
+<p align="center">
+  <img src="validation_report.png" alt="Validation Report" width="100%">
+</p>
+
+## 5. Usage
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("electricsheepafrica/synthetic-tb-screening-symptoms-genexpert-WHO", "moderate_tb_burden")
+df = dataset["train"].to_pandas()
+```
+
+## 6. Limitations
+
+- **Synthetic**: Not for clinical use or diagnostic algorithm validation.
+- **No paediatric TB**: Only adults ≥15 years; paediatric TB has different presentation.
+- **Simplified CXR**: CXR findings are categorical; no image data.
+- **No treatment timeline**: Outcomes are assigned, not modelled over time.
+- **Single sputum sample**: Real diagnosis often requires multiple specimens.
+
+## 7. References
+
+1. WHO (2023). Global Tuberculosis Report 2023. Geneva.
+2. WHO (2021). Consolidated guidelines on TB: Rapid diagnostics. Geneva.
+3. Corbett EL, et al. (2003). TB and the HIV epidemic. *Arch Intern Med*, 163:1009.
+4. Steingart KR, et al. (2014). Xpert MTB/RIF for pulmonary TB. *Cochrane*, CD009593.
+5. Getahun H, et al. (2007). Smear-negative pulmonary TB in HIV. *Lancet ID*, 7(4):238-246.
+6. WHO (2013). Systematic screening for active TB. Geneva.
+7. van't Hoog AH, et al. (2012). Screening strategies for TB. *PLoS One*, 7(5):e36392.
+
+## Citation
+
+```bibtex
+@dataset{esa_tb_2025,
+  title={Synthetic TB Screening and Symptom Dataset},
+  author={Electric Sheep Africa},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/electricsheepafrica/synthetic-tb-screening-symptoms-genexpert-WHO}
+}
+```
+
+## 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 Synthetic TB Screening & Symptom Dataset Generator
+======================================================================
+
+Generates realistic synthetic datasets of patients undergoing tuberculosis
+screening at LMIC health facilities, with demographic data, symptom profiles,
+HIV co-infection status, diagnostic results (smear, GeneXpert, chest X-ray),
+and TB classification.
+
+Target population: Adults and adolescents (≥15 years) presenting for TB
+screening at facility-based care in TB-endemic LMIC settings.
+
+DAG (Sampling Order):
+  1. age, sex (roots)
+  2. hiv_status (scenario-dependent, age/sex-adjusted)
+  3. bmi (conditional on age, TB status)
+  4. true_tb_status (from prevalence, conditional on HIV)
+  5. tb_type (pulmonary vs extrapulmonary)
+  6. symptoms: cough, fever, night_sweats, weight_loss, hemoptysis
+  7. smear_result, genexpert_result, cxr_finding (conditional on TB + test performance)
+  8. tb_classification (derived)
+
+References:
+-----------
+[1] WHO (2023). Global Tuberculosis Report 2023. Geneva.
+[2] WHO (2021). WHO consolidated guidelines on tuberculosis: Module 3 -
+    Diagnosis: Rapid diagnostics for TB detection. Geneva.
+[3] Corbett EL, et al. (2003). The growing burden of tuberculosis: global
+    trends and interactions with the HIV epidemic. Arch Intern Med, 163:1009.
+[4] Steingart KR, et al. (2014). Xpert MTB/RIF assay for pulmonary TB and
+    rifampicin resistance in adults. Cochrane Database Syst Rev, 1:CD009593.
+[5] Getahun H, et al. (2007). Diagnosis of smear-negative pulmonary TB in
+    people with HIV infection. Lancet Infect Dis, 7(4):238-246.
+[6] WHO (2013). Systematic screening for active TB: principles and
+    recommendations. Geneva.
+[7] van't Hoog AH, et al. (2012). Screening strategies for TB prevalence
+    surveys. PLoS One, 7(5):e36392.
+[8] UNAIDS (2023). Global HIV & AIDS statistics fact sheet.
+"""
+
+import numpy as np
+import pandas as pd
+from scipy.stats import truncnorm
+import argparse
+import os
+
+# ============================================================
+# SECTION 1: Literature-Informed Parameters
+# ============================================================
+
+SCENARIOS = {
+    'low_tb_burden': {
+        'description': 'Lower TB incidence LMIC (e.g., urban Latin America)',
+        'tb_prevalence_screened': 0.05,  # WHO 2023: 3-8% among symptomatic screened
+        'hiv_prevalence': 0.03,
+        'hiv_tb_coinfection': 0.15,      # 10-20% of TB is HIV+ in low-HIV settings
+        'mdr_rate': 0.03,               # WHO 2023: 2-5% new cases
+        'smear_positive_pct': 0.55,      # ~50-60% of PTB is smear+
+    },
+    'moderate_tb_burden': {
+        'description': 'Moderate TB burden (e.g., Kenya, India, Philippines)',
+        'tb_prevalence_screened': 0.12,  # WHO 2023: 8-15% among presumptive TB
+        'hiv_prevalence': 0.08,
+        'hiv_tb_coinfection': 0.30,      # Corbett 2003: 25-40% in SSA
+        'mdr_rate': 0.05,
+        'smear_positive_pct': 0.50,
+    },
+    'high_tb_burden': {
+        'description': 'High TB/HIV burden (e.g., South Africa, Mozambique, DRC)',
+        'tb_prevalence_screened': 0.22,  # WHO 2023: 15-30% in high-burden
+        'hiv_prevalence': 0.20,
+        'hiv_tb_coinfection': 0.50,      # Corbett 2003: 40-60% in high HIV
+        'mdr_rate': 0.08,               # WHO 2023: up to 10% in hotspots
+        'smear_positive_pct': 0.40,      # Lower smear+ in HIV coinfection
+    },
+}
+
+# --- GeneXpert Performance ---
+# Source: Steingart 2014, WHO 2021
+XPERT_SENSITIVITY_SMEAR_POS = 0.98   # Smear-positive PTB
+XPERT_SENSITIVITY_SMEAR_NEG = 0.68   # Smear-negative PTB
+XPERT_SENSITIVITY_HIV = 0.79         # HIV-associated TB (pooled)
+XPERT_SPECIFICITY = 0.98
+XPERT_RIF_SENSITIVITY = 0.95         # For rifampicin resistance detection
+
+# --- Smear Microscopy Performance ---
+# Source: Getahun 2007
+SMEAR_SENSITIVITY = 0.60             # Overall
+SMEAR_SENSITIVITY_HIV = 0.40         # Reduced in HIV+
+SMEAR_SPECIFICITY = 0.98
+
+# --- CXR Performance ---
+# Source: van't Hoog 2012
+CXR_SENSITIVITY = 0.87               # For active PTB
+CXR_SPECIFICITY = 0.70               # Many false positives (old TB, other pathology)
+
+# --- Symptom Profiles ---
+# Source: WHO 2013 systematic screening guidelines, van't Hoog 2012
+SYMPTOM_PROB_TB = {
+    'cough_2weeks': 0.75,     # Cough ≥2 weeks
+    'fever': 0.65,
+    'night_sweats': 0.55,
+    'weight_loss': 0.60,
+    'hemoptysis': 0.15,
+    'chest_pain': 0.40,
+    'fatigue': 0.70,
+    'loss_of_appetite': 0.55,
+}
+SYMPTOM_PROB_NO_TB = {
+    'cough_2weeks': 0.10,
+    'fever': 0.15,
+    'night_sweats': 0.05,
+    'weight_loss': 0.08,
+    'hemoptysis': 0.01,
+    'chest_pain': 0.12,
+    'fatigue': 0.20,
+    'loss_of_appetite': 0.10,
+}
+
+# --- BMI ---
+BMI_NORMAL = {'mean': 22.0, 'sd': 3.5}
+BMI_TB = {'mean': 18.5, 'sd': 2.5}  # TB patients often underweight
+
+
+# ============================================================
+# SECTION 2: Utility Functions
+# ============================================================
+
+def trunc_normal(mean, sd, lo, hi, size, rng):
+    a, b = (lo - mean) / sd, (hi - mean) / sd
+    return truncnorm.rvs(a, b, loc=mean, scale=sd, size=size,
+                         random_state=rng.integers(0, 2**31))
+
+
+# ============================================================
+# SECTION 3: Main Generator
+# ============================================================
+
+def generate_tb_dataset(n=10000, seed=42, scenario='moderate_tb_burden'):
+    rng = np.random.default_rng(seed)
+    sc = SCENARIOS[scenario]
+
+    # ── Step 1: Demographics ──
+    sex = rng.choice(['M', 'F'], size=n, p=[0.58, 0.42])  # TB M:F ~1.4:1 (WHO 2023)
+    age = trunc_normal(38, 14, 15, 85, n, rng).astype(int)
+
+    # ── Step 2: HIV status ──
+    hiv_status = np.zeros(n, dtype=int)
+    for i in range(n):
+        p_hiv = sc['hiv_prevalence']
+        if 20 <= age[i] <= 45:
+            p_hiv *= 1.5  # Peak HIV age
+        if sex[i] == 'F' and 20 <= age[i] <= 35:
+            p_hiv *= 1.3  # Higher in young women in SSA
+        hiv_status[i] = 1 if rng.random() < min(p_hiv, 0.50) else 0
+
+    # ── Step 3: True TB status ──
+    true_tb = np.zeros(n, dtype=int)
+    for i in range(n):
+        p_tb = sc['tb_prevalence_screened']
+        if hiv_status[i]:
+            p_tb *= 3.0  # HIV is strongest risk factor (Corbett 2003)
+        if age[i] > 50:
+            p_tb *= 1.3
+        if sex[i] == 'M':
+            p_tb *= 1.2
+        true_tb[i] = 1 if rng.random() < min(p_tb, 0.60) else 0
+
+    # ── Step 4: TB type and drug resistance ──
+    tb_type = np.array(['none'] * n, dtype=object)
+    rifampicin_resistant = np.zeros(n, dtype=int)
+    for i in range(n):
+        if true_tb[i]:
+            # ~85% pulmonary, ~15% extrapulmonary (higher in HIV+)
+            p_eptb = 0.25 if hiv_status[i] else 0.12
+            tb_type[i] = 'extrapulmonary' if rng.random() < p_eptb else 'pulmonary'
+            rifampicin_resistant[i] = 1 if rng.random() < sc['mdr_rate'] else 0
+
+    # ── Step 5: BMI ──
+    bmi = np.zeros(n)
+    for i in range(n):
+        if true_tb[i]:
+            bmi[i] = rng.normal(BMI_TB['mean'], BMI_TB['sd'])
+        else:
+            bmi[i] = rng.normal(BMI_NORMAL['mean'], BMI_NORMAL['sd'])
+    bmi = np.clip(np.round(bmi, 1), 12.0, 45.0)
+
+    # ── Step 6: Symptoms ──
+    symptoms = {}
+    for sym in SYMPTOM_PROB_TB:
+        symptoms[sym] = np.zeros(n, dtype=int)
+        for i in range(n):
+            p = SYMPTOM_PROB_TB[sym] if true_tb[i] else SYMPTOM_PROB_NO_TB[sym]
+            # HIV+ TB patients may have fewer respiratory symptoms
+            if true_tb[i] and hiv_status[i] and sym in ('cough_2weeks', 'hemoptysis'):
+                p *= 0.80
+            symptoms[sym][i] = 1 if rng.random() < p else 0
+
+    # Cough duration (weeks)
+    cough_duration_weeks = np.zeros(n, dtype=int)
+    for i in range(n):
+        if symptoms['cough_2weeks'][i]:
+            if true_tb[i]:
+                cough_duration_weeks[i] = max(2, rng.poisson(5))
+            else:
+                cough_duration_weeks[i] = max(2, rng.poisson(3))
+    cough_duration_weeks = np.clip(cough_duration_weeks, 0, 52)
+
+    # Number of WHO screening symptoms (cough ≥2wk, fever, night sweats, weight loss)
+    who_symptom_count = (symptoms['cough_2weeks'] + symptoms['fever'] +
+                          symptoms['night_sweats'] + symptoms['weight_loss'])
+
+    # ── Step 7: Smear microscopy ──
+    smear_result = np.zeros(n, dtype=int)
+    for i in range(n):
+        if true_tb[i] and tb_type[i] == 'pulmonary':
+            sens = SMEAR_SENSITIVITY_HIV if hiv_status[i] else SMEAR_SENSITIVITY
+            smear_result[i] = 1 if rng.random() < sens else 0
+        elif true_tb[i] and tb_type[i] == 'extrapulmonary':
+            smear_result[i] = 1 if rng.random() < 0.10 else 0  # Very low for EPTB
+        else:
+            smear_result[i] = 1 if rng.random() < (1 - SMEAR_SPECIFICITY) else 0
+
+    # ── Step 8: GeneXpert MTB/RIF ──
+    xpert_mtb = np.zeros(n, dtype=int)
+    xpert_rif_resistant = np.array(['N/A'] * n, dtype=object)
+    for i in range(n):
+        if true_tb[i] and tb_type[i] == 'pulmonary':
+            if smear_result[i]:
+                sens = XPERT_SENSITIVITY_SMEAR_POS
+            elif hiv_status[i]:
+                sens = XPERT_SENSITIVITY_HIV
+            else:
+                sens = XPERT_SENSITIVITY_SMEAR_NEG
+            xpert_mtb[i] = 1 if rng.random() < sens else 0
+        elif true_tb[i] and tb_type[i] == 'extrapulmonary':
+            xpert_mtb[i] = 1 if rng.random() < 0.40 else 0
+        else:
+            xpert_mtb[i] = 1 if rng.random() < (1 - XPERT_SPECIFICITY) else 0
+
+        if xpert_mtb[i]:
+            if rifampicin_resistant[i]:
+                xpert_rif_resistant[i] = 'detected' if rng.random() < XPERT_RIF_SENSITIVITY else 'not_detected'
+            else:
+                xpert_rif_resistant[i] = 'not_detected' if rng.random() < 0.98 else 'detected'
+
+    # ── Step 9: Chest X-ray ──
+    cxr_result = np.array(['normal'] * n, dtype=object)
+    for i in range(n):
+        if true_tb[i] and tb_type[i] == 'pulmonary':
+            if rng.random() < CXR_SENSITIVITY:
+                cxr_result[i] = rng.choice(['infiltrate', 'cavity', 'miliary',
+                                             'pleural_effusion'],
+                                            p=[0.50, 0.25, 0.10, 0.15])
+        elif true_tb[i] and tb_type[i] == 'extrapulmonary':
+            if rng.random() < 0.30:
+                cxr_result[i] = rng.choice(['infiltrate', 'pleural_effusion'],
+                                            p=[0.40, 0.60])
+        else:
+            if rng.random() < (1 - CXR_SPECIFICITY):
+                cxr_result[i] = rng.choice(['old_tb_scar', 'other_pathology',
+                                             'infiltrate'],
+                                            p=[0.40, 0.40, 0.20])
+
+    cxr_abnormal = (cxr_result != 'normal').astype(int)
+
+    # ── Step 10: TB classification ──
+    tb_classification = np.array(['not_tb'] * n, dtype=object)
+    for i in range(n):
+        if true_tb[i]:
+            if tb_type[i] == 'pulmonary' and smear_result[i]:
+                tb_classification[i] = 'smear_positive_ptb'
+            elif tb_type[i] == 'pulmonary':
+                tb_classification[i] = 'smear_negative_ptb'
+            else:
+                tb_classification[i] = 'extrapulmonary_tb'
+
+    # ── Step 11: Treatment outcome (simplified) ──
+    treatment_outcome = np.array(['not_applicable'] * n, dtype=object)
+    for i in range(n):
+        if true_tb[i]:
+            if rifampicin_resistant[i]:
+                outcomes = ['cured', 'completed', 'failed', 'died', 'lost_to_followup']
+                probs = [0.45, 0.15, 0.15, 0.15, 0.10]
+            elif hiv_status[i]:
+                outcomes = ['cured', 'completed', 'failed', 'died', 'lost_to_followup']
+                probs = [0.50, 0.20, 0.05, 0.15, 0.10]
+            else:
+                outcomes = ['cured', 'completed', 'failed', 'died', 'lost_to_followup']
+                probs = [0.65, 0.20, 0.03, 0.05, 0.07]
+            treatment_outcome[i] = rng.choice(outcomes, p=probs)
+
+    # ── Assemble DataFrame ──
+    df = pd.DataFrame({
+        'id': np.arange(1, n + 1),
+        'age_years': age,
+        'sex': sex,
+        'bmi': bmi,
+        'hiv_status': hiv_status,
+        'cough_2weeks': symptoms['cough_2weeks'],
+        'cough_duration_weeks': cough_duration_weeks,
+        'fever': symptoms['fever'],
+        'night_sweats': symptoms['night_sweats'],
+        'weight_loss': symptoms['weight_loss'],
+        'hemoptysis': symptoms['hemoptysis'],
+        'chest_pain': symptoms['chest_pain'],
+        'fatigue': symptoms['fatigue'],
+        'loss_of_appetite': symptoms['loss_of_appetite'],
+        'who_symptom_screen_count': who_symptom_count,
+        'smear_result': smear_result,
+        'xpert_mtb_detected': xpert_mtb,
+        'xpert_rif_resistance': xpert_rif_resistant,
+        'cxr_result': cxr_result,
+        'cxr_abnormal': cxr_abnormal,
+        'true_tb_status': true_tb,
+        'tb_type': tb_type,
+        'tb_classification': tb_classification,
+        'rifampicin_resistant': rifampicin_resistant,
+        'treatment_outcome': treatment_outcome,
+    })
+
+    # ── Print summary ──
+    tb_pos = true_tb.sum()
+    print(f"\n{'='*60}")
+    print(f"TB Screening — {scenario} (n={n}, seed={seed})")
+    print(f"{'='*60}")
+    print(f"\nTB prevalence: {tb_pos/n*100:.1f}%")
+    print(f"HIV+: {hiv_status.mean()*100:.1f}%")
+    print(f"TB-HIV co-infection: {(true_tb & hiv_status).sum()}/{tb_pos} "
+          f"({(true_tb & hiv_status).sum()/max(tb_pos,1)*100:.0f}%) of TB cases")
+    print(f"Pulmonary TB: {(tb_type=='pulmonary').sum()}")
+    print(f"Extrapulmonary TB: {(tb_type=='extrapulmonary').sum()}")
+    print(f"Smear+: {smear_result.sum()} ({smear_result.mean()*100:.1f}%)")
+    print(f"Xpert MTB+: {xpert_mtb.sum()} ({xpert_mtb.mean()*100:.1f}%)")
+    print(f"CXR abnormal: {cxr_abnormal.sum()} ({cxr_abnormal.mean()*100:.1f}%)")
+    print(f"MDR-TB: {rifampicin_resistant.sum()} ({rifampicin_resistant[true_tb==1].mean()*100:.1f}% of TB)")
+    print(f"Mean BMI (TB): {bmi[true_tb==1].mean():.1f}, (non-TB): {bmi[true_tb==0].mean():.1f}")
+
+    return df
+
+
+# ============================================================
+# SECTION 4: CLI Entry Point
+# ============================================================
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description='Generate synthetic TB screening dataset')
+    parser.add_argument('--scenario', type=str, default='moderate_tb_burden',
+                        choices=list(SCENARIOS.keys()))
+    parser.add_argument('--n', type=int, default=10000)
+    parser.add_argument('--seed', type=int, default=42)
+    parser.add_argument('--output', type=str, default=None)
+    parser.add_argument('--all-scenarios', action='store_true')
+    args = parser.parse_args()
+
+    os.makedirs('data', exist_ok=True)
+
+    if args.all_scenarios:
+        for sc_name in SCENARIOS:
+            df = generate_tb_dataset(n=args.n, seed=args.seed, scenario=sc_name)
+            out = os.path.join('data', f'tb_{sc_name}.csv')
+            df.to_csv(out, index=False)
+            print(f"  → Saved to {out}\n")
+    else:
+        df = generate_tb_dataset(n=args.n, seed=args.seed, scenario=args.scenario)
+        out = args.output or os.path.join('data', f'tb_{args.scenario}.csv')
+        df.to_csv(out, index=False)
+        print(f"  → Saved to {out}")

requirements.txt

@@ -0,0 +1,4 @@
+numpy>=1.24
+pandas>=2.0
+scipy>=1.10
+matplotlib>=3.7

validate_dataset.py

@@ -0,0 +1,143 @@
+#!/usr/bin/env python3
+"""Validation & Diagnostic Visualization for TB Screening Dataset."""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+SCENARIOS = ['low_tb_burden', 'moderate_tb_burden', 'high_tb_burden']
+
+
+def load_scenarios(data_dir='data'):
+    dfs = {}
+    for sc in SCENARIOS:
+        path = os.path.join(data_dir, f'tb_{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, 22))
+    fig.suptitle('TB Screening & Symptom Data — Validation Report',
+                 fontsize=16, fontweight='bold', y=0.98)
+    df = dfs.get('moderate_tb_burden', list(dfs.values())[0])
+
+    # Panel 1: TB classification
+    ax = axes[0, 0]
+    order = ['not_tb', 'smear_positive_ptb', 'smear_negative_ptb', 'extrapulmonary_tb']
+    colors = ['#2ecc71', '#e74c3c', '#f39c12', '#9b59b6']
+    counts = df['tb_classification'].value_counts().reindex(order).fillna(0)
+    ax.bar(range(4), counts.values, color=colors)
+    ax.set_xticks(range(4))
+    ax.set_xticklabels(['Not TB', 'Smear+ PTB', 'Smear- PTB', 'EPTB'], fontsize=9)
+    for i, v in enumerate(counts.values):
+        ax.text(i, v + 30, f'{v/len(df)*100:.1f}%', ha='center', fontsize=10)
+    ax.set_ylabel('Count')
+    ax.set_title('TB Classification (Moderate Burden)')
+
+    # Panel 2: Symptom prevalence TB vs non-TB
+    ax = axes[0, 1]
+    syms = ['cough_2weeks', 'fever', 'night_sweats', 'weight_loss', 'hemoptysis', 'fatigue']
+    tb = df[df['true_tb_status'] == 1]
+    no_tb = df[df['true_tb_status'] == 0]
+    x = np.arange(len(syms))
+    ax.barh(x - 0.2, [tb[s].mean()*100 for s in syms], 0.4, label='TB+', color='#e74c3c', alpha=0.8)
+    ax.barh(x + 0.2, [no_tb[s].mean()*100 for s in syms], 0.4, label='No TB', color='#3498db', alpha=0.8)
+    ax.set_yticks(x)
+    ax.set_yticklabels([s.replace('_', ' ').title() for s in syms], fontsize=8)
+    ax.set_xlabel('Prevalence (%)')
+    ax.set_title('Symptom Prevalence: TB+ vs No TB')
+    ax.legend(fontsize=9)
+
+    # Panel 3: BMI distribution by TB status
+    ax = axes[1, 0]
+    ax.hist(no_tb['bmi'], bins=40, alpha=0.6, color='#3498db', label='No TB', edgecolor='white')
+    ax.hist(tb['bmi'], bins=40, alpha=0.6, color='#e74c3c', label='TB+', edgecolor='white')
+    ax.axvline(18.5, color='black', ls='--', lw=1, label='Underweight <18.5')
+    ax.set_xlabel('BMI')
+    ax.set_title('BMI Distribution by TB Status')
+    ax.legend(fontsize=8)
+
+    # Panel 4: Diagnostic cascade
+    ax = axes[1, 1]
+    metrics = ['True TB+', 'CXR Abnormal', 'Xpert MTB+', 'Smear+']
+    vals = [(df['true_tb_status']==1).sum(), df['cxr_abnormal'].sum(),
+            df['xpert_mtb_detected'].sum(), df['smear_result'].sum()]
+    ax.barh(range(4), vals, color=['#2ecc71', '#f39c12', '#9b59b6', '#e74c3c'], alpha=0.8)
+    ax.set_yticks(range(4))
+    ax.set_yticklabels(metrics)
+    for i, v in enumerate(vals):
+        ax.text(v + 20, i, f'{v/len(df)*100:.1f}%', va='center', fontsize=10)
+    ax.set_xlabel('Count')
+    ax.set_title('Diagnostic Cascade')
+
+    # Panel 5: Cross-scenario TB prevalence
+    ax = axes[2, 0]
+    x = np.arange(3)
+    width = 0.2
+    for i, sc in enumerate(SCENARIOS):
+        if sc not in dfs:
+            continue
+        d = dfs[sc]
+        tb_r = d['true_tb_status'].mean() * 100
+        hiv_r = d['hiv_status'].mean() * 100
+        mdr_r = d[d['true_tb_status']==1]['rifampicin_resistant'].mean() * 100 if d['true_tb_status'].sum() > 0 else 0
+        ax.bar(x[0] + i*width, tb_r, width, alpha=0.8, label=sc.replace('_', ' ').title() if i == 0 else '')
+        ax.bar(x[1] + i*width, hiv_r, width, alpha=0.8)
+        ax.bar(x[2] + i*width, mdr_r, width, alpha=0.8)
+    ax.set_xticks(x + width)
+    ax.set_xticklabels(['TB Prevalence', 'HIV Prevalence', 'MDR-TB (of TB)'])
+    ax.set_ylabel('%')
+    ax.set_title('Key Rates Across Scenarios')
+    ax.legend(fontsize=8)
+
+    # Panel 6: HIV-TB co-infection
+    ax = axes[2, 1]
+    hiv_pos = df[df['hiv_status']==1]
+    hiv_neg = df[df['hiv_status']==0]
+    tb_hiv_pos = hiv_pos['true_tb_status'].mean()*100
+    tb_hiv_neg = hiv_neg['true_tb_status'].mean()*100
+    ax.bar([0, 1], [tb_hiv_neg, tb_hiv_pos], color=['#3498db', '#e74c3c'], alpha=0.8)
+    ax.set_xticks([0, 1])
+    ax.set_xticklabels(['HIV-', 'HIV+'])
+    ax.set_ylabel('TB Prevalence (%)')
+    ax.set_title(f'TB Prevalence by HIV Status')
+    for i, v in enumerate([tb_hiv_neg, tb_hiv_pos]):
+        ax.text(i, v + 0.5, f'{v:.1f}%', ha='center', fontsize=11)
+
+    # Panel 7: WHO symptom screen count distribution
+    ax = axes[3, 0]
+    for status, color, label in [(1, '#e74c3c', 'TB+'), (0, '#3498db', 'No TB')]:
+        sub = df[df['true_tb_status']==status]['who_symptom_screen_count']
+        ax.hist(sub, bins=range(0, 6), alpha=0.6, color=color, label=label, edgecolor='white')
+    ax.set_xlabel('WHO Symptom Screen Count (0-4)')
+    ax.set_title('WHO Symptom Screen Score')
+    ax.legend(fontsize=10)
+
+    # Panel 8: Treatment outcomes (TB cases only)
+    ax = axes[3, 1]
+    tb_cases = df[df['true_tb_status']==1]
+    if len(tb_cases) > 0:
+        outcomes = ['cured', 'completed', 'failed', 'died', 'lost_to_followup']
+        out_colors = ['#2ecc71', '#27ae60', '#f39c12', '#e74c3c', '#95a5a6']
+        counts_out = tb_cases['treatment_outcome'].value_counts().reindex(outcomes).fillna(0)
+        ax.bar(range(5), counts_out.values, color=out_colors)
+        ax.set_xticks(range(5))
+        ax.set_xticklabels([o.replace('_', '\n').title() for o in outcomes], fontsize=8)
+        for i, v in enumerate(counts_out.values):
+            ax.text(i, v + 5, f'{v/len(tb_cases)*100:.0f}%', ha='center', fontsize=9)
+    ax.set_ylabel('Count')
+    ax.set_title('Treatment Outcomes (TB cases)')
+
+    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)