Datasets:

AnodeAI
/

Synthetic-cancer-clinical-genomics

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.

{
  "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:

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.")

Synthetic Cancer Clinical Genomics Dataset

Dataset Structure

Data Fields

1. Patients Table (patients)

2. Genomic Biomarkers Table (genomic_biomarkers)

3. Clinical Encounters Table (clinical_encounters)

4. Financial Claims Table (financial_claims)