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EMI - Bone Tumor, Tumour Detection Dataset, Patient Records (Synthetic)
Patient Health Records & Digital Health
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£199.99
About
The EMI - Bone Tumor, Tumour Detection Dataset, Patient Records (Synthetic) is designed for educational and research purposes to analyze factors associated with bone tumors, their progression, and treatment options. The dataset includes anonymized, synthetic data on various clinical and demographic factors for individuals diagnosed with different types of bone tumors.
Dataset Features
- Participant ID: Unique identifier for each participant.
- Sex: Sex of the participant (Male/Female).
- Age: Age of the participant (in years).
- Grade: Grade of the tumor (Low/Intermediate/High).
- Histological Type: Type of tumor (e.g., pleomorphic sarcoma, synovial sarcoma).
- MSKCC Type: Type of malignancy based on the Memorial Sloan Kettering Cancer Center classification (e.g., Leiomyosarcoma).
- Site of Primary STS: Location of the primary soft tissue sarcoma (e.g., right thigh, left thigh).
- Status: Current status of the participant (NED: No Evidence of Disease, AWD: Alive with Disease, D: Deceased).
- Treatment: Type of treatment received (e.g., Radiotherapy, Surgery, Chemotherapy, or a combination).
Distribution
Usage
This dataset can be used for the following applications:
- Cancer Research: Investigate the relationship between various demographic, clinical, and treatment factors with the presence and progression of bone tumors.
- Predictive Modeling: Build machine learning models to predict tumor status or prognosis based on participant data.
- Clinical Research: Study the impact of tumor grade, histological type, and treatment options on survival and recovery.
- Educational Purposes: Provide a dataset for students and researchers in oncology, medical data science, and healthcare fields to analyze cancer progression and treatment patterns.
Coverage
This synthetic dataset is fully anonymized and complies with data privacy standards. It includes a broad set of factors to support diverse research and analysis in the oncology and medical domains.
License
CC0 (Public Domain)
Who Can Use It
- Cancer Researchers: To explore correlations between clinical factors, tumor types, and treatment outcomes.
- Oncologists and Healthcare Providers: To analyze how tumor characteristics and treatments affect prognosis and survival rates.
- Data Scientists and Machine Learning Practitioners: To develop predictive models for bone tumor diagnosis, progression, and survival.
- Educators and Students: As a resource for studying cancer research analytics and medical data science.
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