Doctor-patient Conversational Dataset
Project Overview:
Objective
Scope
The project encompasses various medical specialties, ranging from general practice to more specialized fields like cardiology and neurology. It includes diverse patient demographics to ensure a comprehensive representation of real-world medical conversations.
Sources
- Data is sourced from consenting participants in various healthcare settings, ensuring confidentiality and ethical compliance. Collaborations with hospitals and clinics across different regions provided access to a rich pool of audio conversations.
Data Collection Metrics
- Total Audio Hours Collected: 1,500 hours
- Number of Unique Conversations: 10,000
- Participant Demographics: 45% male, 55% female, ages ranging from 18 to 85
- Medical Specialties Covered: 15, including General Practice, Pediatrics, Oncology
Annotation Process
Stages
- Transcription: Converting audio files to text.
- Categorization: Classifying conversations based on medical specialty and topics.
- Entity Tagging: Identifying and tagging medical terms, symptoms, and medications.
Annotation Metrics
- Total Conversations Annotated: 10,000
- Total Annotations: 500,000
- Average Annotations per Conversation: 50
Quality Assurance
QA Metrics
- Accuracy of Transcription: 98%
- Consistency in Categorization: 95%
- Precision in Entity Tagging: 97%
Conclusion
The Doctor-Patient Conversational Dataset project is a landmark initiative in the intersection of healthcare and AI. By providing a rich, well-annotated dataset, it paves the way for advancements in conversational AI, ultimately leading to more effective and empathetic patient care.
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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