---
license: cc-by-4.0
task_categories:
- tabular-classification
- regression
tags:
- medical
- oncology
- genomics
- synthetic
- healthcare-claims
pretty_name: Synthetic Cancer Clinical Genomics Dataset
size_categories:
- n<1K
---

# Synthetic Cancer Clinical Genomics Dataset

This repository contains multi-modal synthetic clinical-genomic data designed for oncology tracking, therapeutic progression analysis, and financial cost-modeling. The dataset is organized relationally under four core entities: Patients, Genomic Biomarkers, Clinical Encounters, and Financial Claims.

## Dataset Structure

The dataset is provided as a unified JSON file containing four arrays of structured objects.

```json
{
  "patients": [...],
  "genomic_biomarkers": [...],
  "clinical_encounters": [...],
  "financial_claims": [...]
}
```

---

## Data Fields

### 1. Patients Table (`patients`)
Contains baseline demographics for each unique patient tracked across the clinical timeline.

| Field Name | Type | Description | Example |
| :--- | :--- | :--- | :--- |
| `patient_id` | String | Unique identifier for the patient (Primary Key) | `"PT-60900"` |
| `age_at_diagnosis` | Integer | Patient's age at initial cancer diagnosis | `83` |
| `biological_sex` | String | Confirmed biological sex (`Male`, `Female`) | `"Female"` |
| `race_ethnicity` | String | Demographic group (`White`, `Black`, `Asian`, `Hispanic`, `Other`) | `"Black"` |

### 2. Genomic Biomarkers Table (`genomic_biomarkers`)
Tracks sequencing pipeline metrics, targeted alterations, and immune checkpoint expression values.

| Field Name | Type | Description | Example |
| :--- | :--- | :--- | :--- |
| `genomic_id` | String | Unique identifier for the molecular testing event (Primary Key) | `"G-92403"` |
| `patient_id` | String | Associated patient identifier (Foreign Key linking to `patients`) | `"PT-60900"` |
| `sequencing_date` | String | ISO 8601 date of the genomic profile extraction | `"2025-08-23"` |
| `gene_mutated` | String | Target mutated gene analyzed | `"HER2"` |
| `variant_classification` | String | Specific variant alteration category | `"R273H"` |
| `tmb_score` | Float | Tumor Mutational Burden score (mutations per megabase) | `34.2` |
| `pd_l1_expression_pct` | Integer | Tumor proportion score (TPS) percentage for PD-L1 expression | `29` |

### 3. Clinical Encounters Table (`clinical_encounters`)
Captures serial physical assessments, clinical staging advancements, and real-world therapy switches.

| Field Name | Type | Description | Example |
| :--- | :--- | :--- | :--- |
| `encounter_id` | String | Unique clinical interaction code (Primary Key) | `"ENC-35247"` |
| `patient_id` | String | Associated patient identifier (Foreign Key linking to `patients`) | `"PT-49536"` |
| `date_of_encounter` | String | ISO 8601 date of the specific medical appointment | `"2024-11-14"` |
| `primary_icd10_code` | String | ICD-10 code designating malignant neoplasm site location | `"C34.90"` |
| `cancer_stage` | String | Clinical disease stage evaluated during the encounter | `"Stage IV"` |
| `treatment_line` | Integer | Active line of systemic antineoplastic therapy | `1` |
| `medication_rxnorm` | String | RxNorm Concept Unique Identifier (CUI) for prescribed medication | `"2145574"` |
| `progression_free_survival_status` | Integer | Binary indicator for clinical progression event (1 = Progressed, 0 = Stable) | `1` |

### 4. Financial Claims Table (`financial_claims`)
Captures cost profiles, contractually allowed medical insurance amounts, and liability balances.

| Field Name | Type | Description | Example |
| :--- | :--- | :--- | :--- |
| `claim_id` | String | Unique invoice/billing system item (Primary Key) | `"CLM-98596"` |
| `patient_id` | String | Associated patient identifier (Foreign Key linking to `patients`) | `"PT-49536"` |
| `encounter_id` | String | Linked clinical encounter (Foreign Key linking to `clinical_encounters`) | `"ENC-35247"` |
| `allowed_amount` | Float | Maximum negotiated contract value allowed by the payor ($) | `18622.89` |
| `out_of_pocket_cost` | Float | Patient out-of-pocket financial liability ($) | `677.35` |

---

## Intended Use Cases

* **Survival Analysis:** Modeling Real-World Progression-Free Survival (rwPFS) trends utilizing biomarker cohorts (`tmb_score`, `gene_mutated`).
* **Health Economics and Outcomes Research (HEOR):** Aggregating longitudinal out-of-pocket balances and total allowed claims costs linked directly to specific therapy lines (`treatment_line`).
* **Relational Feature Engineering:** Evaluating categorical entity embeddings via multi-table merge architectures.

---

## Data Loading Quickstart

Load the entire schema into separate tabular `pandas` DataFrames via Python:

```python
import json
import pandas as pd

# Load relational file
with open("oncology_data_batch_4.json", "r") as file:
    data = json.load(file)

# Parse distinct arrays into relational DataFrames
df_patients = pd.DataFrame(data["patients"])
df_genomics = pd.DataFrame(data["genomic_biomarkers"])
df_encounters = pd.DataFrame(data["clinical_encounters"])
df_claims = pd.DataFrame(data["financial_claims"])

print(f"Loaded {len(df_patients)} patients and {len(df_genomics)} biomarker profiles.")
```