electricsheepafrica/africa-synth-tuberculosis-chest-ctscan-african-ehr-all

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Chest CT Scans + Synthetic African EHR (Lung Cancer)

A multimodal lung cancer dataset pairing 1,000 de-identified chest CT scan images with class-conditional synthetic Electronic Health Records (EHRs) calibrated to sub-Saharan and North African populations. Every CT image is linked to a unique synthetic patient with ~60 clinical, demographic, exposure, and laboratory fields designed to match the epidemiology of lung cancer in Africa.

Part of the Electric Sheep Africa — Healthcare Collection.

⚠️ The EHR fields are SYNTHETIC. See SYNTHETIC_NOTICE.md for the full ethics and limitations statement. No real patient data were used to construct the EHR.

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Why this dataset exists

Most publicly available lung cancer imaging datasets are image-only, and virtually all multimodal imaging+EHR datasets are derived from North American or European cohorts. This leaves a gap:

• African lung cancer epidemiology differs materially from Western reference series (younger age at diagnosis, more never-smoker cancer, major role of biomass-fuel exposure and TB sequelae, non-trivial HIV burden, later stage at presentation).
• Models trained on Western data do not automatically generalize to African patients.
• Researchers working on African clinical AI lack paired multimodal benchmarks for prototyping, teaching, and stress-testing.

This dataset provides a reproducible synthetic sandbox for that gap. The images are real and de-identified; the EHR is clearly synthetic, deterministically generated, and statistically grounded in published African epidemiology.

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Contents

File	Rows	Description
patients.csv	1000	One row per patient; contains all EHR fields plus the image path
ehr_only.csv	1000	Same as above without image-path columns, for pure tabular workflows
data_dictionary.csv	~60	Schema, type, source (label / ground-truth / synthetic), description for every field
images/{split}/{class}/<patient_id>.png	1000	CT images restructured with stable IDs
SYNTHETIC_NOTICE.md	—	Ethics, intended use, and limitations

Splits (from the original Kaggle release)

split	adenocarcinoma	squamous_cell_carcinoma	large_cell_carcinoma	normal	total
train	195	155	115	148	613
valid	23	15	21	13	72
test	120	90	51	54	315
total	338	260	187	215	1000

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Source provenance

Layer	Source	License
CT images	mohamedhanyyy/chest-ctscan-images on Kaggle	ODbL-1.0
EHR fields	Synthetic — generated by the included generate_ehr.py with seed 20260409, using class-conditional distributions calibrated to African lung cancer epidemiology	—
TNM staging (train/valid)	Ground-truth labels embedded in the original Kaggle folder names (e.g. adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib)	—
TNM staging (test)	Sampled from African late-presentation stage distributions	Synthetic

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The Africa-specific priors

Priors differ from Western reference series in the following load-bearing ways. Sources listed below the table.

Aspect	Western reference	This dataset (Africa-calibrated)
Mean age at cancer diagnosis	~68 yr	~58–62 yr
Never-smokers with adenocarcinoma	~25%	~40%
Biomass-fuel cooking exposure	Rarely recorded	~45–55% of cancer patients, higher in women and rural settings
Past pulmonary TB	Rare	Class-dependent prior, scaled by WHO regional TB prevalence
HIV co-infection	Rare	Southern Africa: ~18% base; other sub-regions per UNAIDS 2023
Stage at presentation	Stage I–II common	~66% present at stage IIIA/IIIB/IV (late presentation)
Screening referrals	Common (LDCT programs)	<10% (limited screening infrastructure)
Baseline hemoglobin	~13.5 g/dL	~11–12 g/dL (African anemia burden)

Country / regional distribution

Synthetic patients are assigned an African country with weights reflecting where hospital-based African lung cancer case series have historically been published:

Country	n	Country	n
South Africa	200	Ghana	57
Nigeria	140	Tanzania	56
Egypt	105	Uganda	45
Kenya	99	Algeria	36
Ethiopia	74	Others (Cameroon, Senegal, Tunisia, Morocco, Zimbabwe, Côte d'Ivoire)	~188

Sub-region field (region) collapses these into North / West / East / Central / Southern Africa, which drives the HIV and TB prevalence priors.

Literature used to calibrate priors

• Adeloye D et al. An estimate of the prevalence of lung cancer in Africa. J Glob Health 2016;6(2):020409.
• Koegelenberg CFN et al. The current burden and epidemiology of lung cancer in Africa. J Thorac Dis 2019.
• Mbulaiteye SM et al. HIV/AIDS-related cancers in Africa. Infect Agent Cancer 2011.
• Gordon SB et al. Respiratory risks from household air pollution in LMICs. Lancet Respir Med 2014;2(10):823–60.
• GLOBOCAN 2022 (IARC) — Africa incidence & mortality estimates.
• Parkin DM et al. Cancer in sub-Saharan Africa. IARC Scientific Publications No. 167.
• WHO Global Tuberculosis Report 2023.
• UNAIDS 2023 — adult HIV prevalence by sub-region.

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Schema highlights

The full schema is in data_dictionary.csv (60+ fields). Here are the highlights:

Labels

• diagnosis_class — adenocarcinoma | squamous_cell_carcinoma | large_cell_carcinoma | normal
• diagnosis_label — Lung Cancer | No Malignancy
• stage_group, t_stage, n_stage, m_stage — TNM (ground-truth where encoded in source folder, synthetic otherwise)
• tumor_location — anatomical location

Geography & setting

• country, region, setting (Urban / Peri-urban / Rural)

Demographics & exposures

• age, sex, bmi
• smoking_status, pack_years, years_since_quit
• biomass_fuel_exposure, biomass_exposure_years
• occupational_dust_exposure, occupation_high_risk
• past_pulmonary_tb, tb_treatment_completed
• hiv_status, on_antiretroviral_therapy, cd4_count_cells_uL

Comorbidities

• copd, emphysema, hypertension, diabetes_type2, coronary_artery_disease
• family_hx_lung_cancer, prior_cancer_any

Symptoms

• cough, hemoptysis, chest_pain, dyspnea, weight_loss, fatigue, symptom_duration_weeks

Vitals

• sbp_mmHg, dbp_mmHg, heart_rate_bpm, resp_rate_bpm, temp_C, spo2_pct

Laboratory

• CBC: hemoglobin_g_dL, wbc_10e9_L, platelets_10e9_L
• Chemistry: sodium_mmol_L, potassium_mmol_L, creatinine_mg_dL, calcium_mg_dL, albumin_g_dL
• Inflammation: ldh_U_L, crp_mg_L, esr_mm_hr
• Tumor markers: cea_ng_mL, cyfra21_1_ng_mL, nse_ng_mL

Pulmonary function

• fev1_pct_predicted, fvc_pct_predicted, fev1_fvc_ratio

Functional status & workflow

• ecog_performance_status (0–4)
• referral_reason

Imaging metadata

• ct_scanner, slice_thickness_mm, contrast_used, kvp

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Statistical sanity checks

Some class-conditional statistics produced by the generator (directly readable from patients.csv):

Mean age by class (years)

class	mean	std
adenocarcinoma	57.7	11.0
squamous_cell_carcinoma	60.8	9.8
large_cell_carcinoma	58.5	10.0
normal	47.0	12.4

Smoking status by class

class	Never	Former	Current
adenocarcinoma	0.35	0.43	0.22
squamous_cell_carcinoma	0.08	0.40	0.52
large_cell_carcinoma	0.10	0.36	0.54
normal	0.71	0.19	0.10

HIV-positive fraction by region

region	HIV+
Southern Africa	0.276
Central Africa	0.167
East Africa	0.088
West Africa	0.042
North Africa	0.000

Stage at presentation (cancer cases) Stage III/IV combined: ~63% — consistent with published African case series reporting that the majority of patients present at locally advanced or metastatic stage.

Mean FEV₁ %-predicted by class

class	mean
adenocarcinoma	72.1
squamous_cell_carcinoma	57.3
large_cell_carcinoma	59.1
normal	88.7

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Usage

Load the EHR table

from huggingface_hub import hf_hub_download
import pandas as pd

csv_path = hf_hub_download(
    repo_id="electricsheepafrica/chest-ctscan-african-ehr",
    filename="patients.csv",
    repo_type="dataset",
)
df = pd.read_csv(csv_path)
print(df.shape, df["diagnosis_class"].value_counts())

Load an individual CT image

from huggingface_hub import hf_hub_download
from PIL import Image

row = df.iloc[0]
img_path = hf_hub_download(
    repo_id="electricsheepafrica/chest-ctscan-african-ehr",
    filename=row["image_path"],
    repo_type="dataset",
)
img = Image.open(img_path)
img.show()

Snapshot the whole dataset locally

from huggingface_hub import snapshot_download

local = snapshot_download(
    repo_id="electricsheepafrica/chest-ctscan-african-ehr",
    repo_type="dataset",
)

Minimal tabular baseline

import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import classification_report

df = pd.read_csv("patients.csv")

train = df[df.split == "train"]
test  = df[df.split == "test"]

feat_cols = [
    "age", "sex", "bmi", "smoking_status", "pack_years",
    "biomass_fuel_exposure", "biomass_exposure_years",
    "past_pulmonary_tb", "hiv_status",
    "copd", "emphysema", "cough", "hemoptysis", "weight_loss",
    "hemoglobin_g_dL", "albumin_g_dL", "ldh_U_L", "crp_mg_L",
    "cea_ng_mL", "cyfra21_1_ng_mL", "nse_ng_mL",
    "fev1_pct_predicted", "ecog_performance_status",
]
X_tr = pd.get_dummies(train[feat_cols])
y_tr = (train["diagnosis_class"] != "normal").astype(int)
X_te = pd.get_dummies(test[feat_cols]).reindex(columns=X_tr.columns, fill_value=0)
y_te = (test["diagnosis_class"] != "normal").astype(int)

clf = GradientBoostingClassifier(random_state=42).fit(X_tr, y_tr)
print(classification_report(y_te, clf.predict(X_te)))

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Suggested research tasks

1. Multimodal fusion: Compare late-fusion (image CNN + tabular MLP) vs. early-fusion vs. cross-attention architectures on the four-class task.
2. Tabular-only baselines: Establish how far structured features alone go (CEA/CYFRA + FEV₁ + smoking + weight loss are highly informative).
3. Subgroup analysis: Evaluate model calibration across regions, sexes, HIV status, smoking status.
4. Missingness & noise studies: Inject realistic missingness patterns to simulate lower-resource settings and measure degradation.
5. Staging regression: Predict AJCC stage group from image + EHR combined.
6. Domain-shift stress tests: Train on Western-prior synthetic data and evaluate on this Africa-prior synthetic data, or vice versa, to quantify transportability.

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Limitations

• EHR is synthetic. Models trained on these fields will learn the generator's structure, not real biology. Do not use for clinical decisions.
• Image provenance is not African. The CT images come from a publicly available Kaggle dataset whose original source hospitals are not Africa-specific. The Africa focus lives entirely in the synthetic EHR layer.
• Class imbalance across splits, especially the validation split (n=72).
• Simplifying assumptions: Covariances between synthetic fields are modeled through a modest set of modifiers (smoking → COPD, HIV → CD4, etc.) but do not reach the full correlation structure of real EHRs.
• TNM for the test split is sampled, not ground-truth; the original test folders do not encode staging.
• One image = one patient: Some of the original Kaggle class folders contain near-duplicate images; each is treated as a distinct synthetic patient.

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Reproducibility

To regenerate the EHR from scratch:

python3 generate_ehr.py

The seed is 20260409. The script is deterministic and will reproduce this exact release byte-for-byte (given the same input images).

The generator script, the priors, and the literature used to calibrate them are all in the script header comment — they are intentionally transparent so future users can audit or modify them.

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Citation

If you use this dataset, please cite both the original image source and this multimodal augmentation:

@misc{electricsheepafrica_chest_ct_african_ehr,
  title        = {Chest CT Scans + Synthetic African EHR (Lung Cancer)},
  author       = {Electric Sheep Africa},
  year         = {2026},
  howpublished = {Hugging Face Datasets},
  url          = {https://huggingface.co/datasets/electricsheepafrica/chest-ctscan-african-ehr}
}

@misc{mohamedhanyyy_chest_ctscan_images,
  title        = {Chest CT-Scan Images Dataset},
  author       = {Mohamed Hany},
  howpublished = {Kaggle},
  url          = {https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images}
}

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License

• Images: ODbL-1.0 (inherited from the upstream Kaggle dataset)
• Synthetic EHR: released under the same ODbL-1.0 terms
• Generator script: MIT-licensed for reuse

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Collection

Part of the Electric Sheep Africa Healthcare Collection — a curated set of clinical, imaging, and epidemiological datasets focused on African health contexts.

👉 huggingface.co/electricsheepafrica