Datasets:
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README.md
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---
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license: cc-by-4.0
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task_categories:
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- tabular-classification
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- regression
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tags:
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- medical
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- oncology
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- genomics
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- synthetic
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- healthcare-claims
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pretty_name: Synthetic Cancer Clinical Genomics Dataset
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size_categories:
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- n<1K
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---
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# Synthetic Cancer Clinical Genomics Dataset
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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.
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## Dataset Structure
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The dataset is provided as a unified JSON file containing four arrays of structured objects.
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```json
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{
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"patients": [...],
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"genomic_biomarkers": [...],
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"clinical_encounters": [...],
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"financial_claims": [...]
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}
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```
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---
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## Data Fields
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### 1. Patients Table (`patients`)
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Contains baseline demographics for each unique patient tracked across the clinical timeline.
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| Field Name | Type | Description | Example |
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| :--- | :--- | :--- | :--- |
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| `patient_id` | String | Unique identifier for the patient (Primary Key) | `"PT-60900"` |
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| `age_at_diagnosis` | Integer | Patient's age at initial cancer diagnosis | `83` |
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| `biological_sex` | String | Confirmed biological sex (`Male`, `Female`) | `"Female"` |
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| `race_ethnicity` | String | Demographic group (`White`, `Black`, `Asian`, `Hispanic`, `Other`) | `"Black"` |
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### 2. Genomic Biomarkers Table (`genomic_biomarkers`)
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Tracks sequencing pipeline metrics, targeted alterations, and immune checkpoint expression values.
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| Field Name | Type | Description | Example |
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| :--- | :--- | :--- | :--- |
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| `genomic_id` | String | Unique identifier for the molecular testing event (Primary Key) | `"G-92403"` |
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| `patient_id` | String | Associated patient identifier (Foreign Key linking to `patients`) | `"PT-60900"` |
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| `sequencing_date` | String | ISO 8601 date of the genomic profile extraction | `"2025-08-23"` |
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| `gene_mutated` | String | Target mutated gene analyzed | `"HER2"` |
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| `variant_classification` | String | Specific variant alteration category | `"R273H"` |
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| `tmb_score` | Float | Tumor Mutational Burden score (mutations per megabase) | `34.2` |
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| `pd_l1_expression_pct` | Integer | Tumor proportion score (TPS) percentage for PD-L1 expression | `29` |
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### 3. Clinical Encounters Table (`clinical_encounters`)
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Captures serial physical assessments, clinical staging advancements, and real-world therapy switches.
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| Field Name | Type | Description | Example |
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| :--- | :--- | :--- | :--- |
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| `encounter_id` | String | Unique clinical interaction code (Primary Key) | `"ENC-35247"` |
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| `patient_id` | String | Associated patient identifier (Foreign Key linking to `patients`) | `"PT-49536"` |
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| `date_of_encounter` | String | ISO 8601 date of the specific medical appointment | `"2024-11-14"` |
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| `primary_icd10_code` | String | ICD-10 code designating malignant neoplasm site location | `"C34.90"` |
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| `cancer_stage` | String | Clinical disease stage evaluated during the encounter | `"Stage IV"` |
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| `treatment_line` | Integer | Active line of systemic antineoplastic therapy | `1` |
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| `medication_rxnorm` | String | RxNorm Concept Unique Identifier (CUI) for prescribed medication | `"2145574"` |
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| `progression_free_survival_status` | Integer | Binary indicator for clinical progression event (1 = Progressed, 0 = Stable) | `1` |
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### 4. Financial Claims Table (`financial_claims`)
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Captures cost profiles, contractually allowed medical insurance amounts, and liability balances.
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| Field Name | Type | Description | Example |
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| :--- | :--- | :--- | :--- |
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| `claim_id` | String | Unique invoice/billing system item (Primary Key) | `"CLM-98596"` |
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| `patient_id` | String | Associated patient identifier (Foreign Key linking to `patients`) | `"PT-49536"` |
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| `encounter_id` | String | Linked clinical encounter (Foreign Key linking to `clinical_encounters`) | `"ENC-35247"` |
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| `allowed_amount` | Float | Maximum negotiated contract value allowed by the payor ($) | `18622.89` |
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| `out_of_pocket_cost` | Float | Patient out-of-pocket financial liability ($) | `677.35` |
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---
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## Intended Use Cases
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* **Survival Analysis:** Modeling Real-World Progression-Free Survival (rwPFS) trends utilizing biomarker cohorts (`tmb_score`, `gene_mutated`).
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* **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`).
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* **Relational Feature Engineering:** Evaluating categorical entity embeddings via multi-table merge architectures.
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---
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## Data Loading Quickstart
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Load the entire schema into separate tabular `pandas` DataFrames via Python:
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```python
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import json
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import pandas as pd
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# Load relational file
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with open("oncology_data_batch_4.json", "r") as file:
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data = json.load(file)
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# Parse distinct arrays into relational DataFrames
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df_patients = pd.DataFrame(data["patients"])
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df_genomics = pd.DataFrame(data["genomic_biomarkers"])
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df_encounters = pd.DataFrame(data["clinical_encounters"])
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df_claims = pd.DataFrame(data["financial_claims"])
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print(f"Loaded {len(df_patients)} patients and {len(df_genomics)} biomarker profiles.")
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```
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