Medical Data Collection
Project Overview:
Objective
Our objective was to gather an extensive and detailed medical dataset, prioritizing accuracy, breadth, and ethical compliance. Additionally, this Medical Data Collection is tailored to support research and development in machine learning and healthcare technologies.
Scope
We meticulously collected a wide array of medical data, ensuring diversity and depth to maximize its utility and relevance for AI applications.
Sources
- Electronic Health Records (EHRs): A crucial component, providing a detailed overview of patient histories.
- Medical Imaging: Including MRI, CT scans, and X-rays, offering critical visual insights.
- Patient Intake Forms: Capturing essential patient-reported information.
- Laboratory Test Results: Providing key data points from various medical tests.
- Clinical Trial Data: Offering insights from controlled medical studies.
Data Collection Metrics
- Total Data Points Collected: 325,000
- Electronic Health Records: 105,000
- Medical Images: 55,000
- Patient Intake Forms: 75,000
- Laboratory Test Results: 65,000
- Clinical Trial Data: 25,000
Annotation Process
Stages
- Data Redaction: Prioritizing patient privacy by anonymizing personal information.
- Categorization: Efficiently organizing data for ease of access and analysis.
- Medical Image Annotation: Detailing key findings in imaging for precise interpretation.
- Lab Result Interpretation: Classifying lab results for immediate understanding.
- Clinical Data Tagging: Identifying crucial elements in clinical trial data for enhanced insights.
Annotation Metrics
- Total Annotations Made: 1,250,000
- Data Redactions: 310,000
- Categorized Entries: 310,000
- Image Annotations: 210,000
- Lab Result Tags: 260,000
- Clinical Data Tags: 160,000
Quality Assurance
Stages
Medical Expert Review: Leveraging healthcare professionals’ expertise for validation.
Consistency Audits: Employing algorithms to ensure annotation accuracy.
Inter-annotator Agreement: Using multiple experts to guarantee annotation uniformity.
Medical Expert Review: Leveraging healthcare professionals’ expertise for validation.
Consistency Audits: Employing algorithms to ensure annotation accuracy.
Inter-annotator Agreement: Using multiple experts to guarantee annotation uniformity.
QA Metrics
- Annotations Reviewed by Experts: 125,000
- Inconsistencies Identified and Rectified: 25,000
Conclusion
Through rigorous processes, a comprehensive medical dataset has been amassed. The emphasis on quality, accuracy, and ethical considerations ensures its reliability and positions it as a valuable asset for the healthcare research and AI community
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
Let's Discuss your Data collection Requirement With Us
To get a detailed estimation of requirements please reach us.