Audio Datasets for Healthcare & Conversational AI

Project Overview:

Objective

To assemble a multifaceted audio dataset tailored for applications in Healthcare and Conversational AI, enabling advanced recognition, comprehension, and interaction capabilities.

Scope

Compilation of relevant audio samples from the healthcare industry, as well as conversational interactions, followed by detailed annotation to ascertain accuracy and quality.

Audio Datasets for Healthcare & Conversational AI
Audio Datasets for Healthcare & Conversational AI
Audio Datasets for Healthcare & Conversational AI
Audio Datasets for Healthcare & Conversational AI

Sources

  • Electronic Health Records (EHRs): A crucial component, providing a detailed overview of patient histories was productively accumulated. Moreover, EHRs facilitate seamless access to patient data.
  • Patient Intake Forms: Additionally, these forms captured essential patient-reported information, ensuring that healthcare providers have accurate initial data.
  • Laboratory Test Results: Furthermore, these results productively provided key data points from various medical tests, contributing to informed medical decisions.
  • Clinical Trial Data: In addition, this data offered insights from controlled medical studies, thereby enhancing the understanding of treatment efficacy.
Audio Datasets for Healthcare & Conversational AI
Audio Datasets for Healthcare & Conversational AI

Data Collection Metrics

  • Total Audio Samples: 200,000
  • Patient-Doctor Consultations: 70,000
  • Health Helpline Recordings: 30,000
  • Medical Seminars and Lectures: 40,000
  • AI-User Interactions: 40,000
  • General Conversational Samples: 20,000

Annotation Process

Stages

  1. Noise Reduction: Filtering out background noise and enhancing voice clarity are crucial for achieving high-quality audio recordings.
  2. Segmentation: Breaking down long recordings into meaningful chunks can significantly enhance the listening experience and improve comprehension.
  3. Transcription: Converting audio data into textual representation can be a complex process.
  4. Entity Recognition: Highlighting medical terms, drug names, conditions, etc.
  5. Intent Recognition: Categorizing the purpose behind each conversational interaction.

Annotation Metrics

  • Total Annotations: 600,000 (3 annotations per audio sample on average)
  • Noise-reduced Samples: 180,000
  • Segmented Chunks: 550,000
  • Transcribed Samples: 200,000
  • Entity Tags: 150,000
  • Intent Tags: 100,000
Audio Datasets for Healthcare & Conversational AI
Audio Datasets for Healthcare & Conversational AI
Audio Datasets for Healthcare & Conversational AI
Audio Datasets for Healthcare & Conversational AI

Quality Assurance

Expert Review: Medical professionals and linguistic experts meticulously reviewed the annotations for accuracy.

Consistency Checks: Automated checks to identify anomalies or inconsistencies in transcriptions are essential for ensuring data accuracy and reliability.

Inter-annotator Agreement: Multiple annotators assessed a portion of the data to ensure consistent and reliable annotations.

QA Metrics

  • Annotations Reviewed by Experts: 60,000 (10% of total annotations)
  • Inconsistencies Identified and Corrected: 12,000 (2% of total annotations)

Conclusion

This ambitious undertaking has, as a result, yielded a robust audio dataset meticulously curated for healthcare and conversational AI applications. Through meticulous collection and annotation, the dataset is, consequently, poised to significantly bolster advancements in AI-driven healthcare solutions and conversational platforms.

quality dataset

Quality Data Creation

Guaranteed TAT​

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified​

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance​

HIPAA Compliance

GDPR Compliance​

GDPR Compliance

Compliance and Security​

Compliance and Security

Let's Discuss your Data collection Requirement With Us

To get a detailed estimation of requirements please reach us.

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