Sold by: John Snow LabsÂ
Deployed on AWS
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Transform medical voice recording to text and extract entities
Overview
The Medical Speech to Text Model converts spoken language into written text, specifically tailored for the medical field. The resulting text is further analyzed and relevant medical entities are extracted together with assertion statuses for those and relations between them.
IMPORTANT USAGE INFORMATION:
After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.
-Charges apply even if the endpoint is idle and not actively processing requests.
-To stop charges, you MUST DELETE the endpoint in your SageMaker console.
-Simply stopping requests will NOT stop billing.
This ensures you are only billed for the time you actively use the service.
Highlights
- **Key Features** * Medical Voice Recognition: Utilizes state-of-the-art voice recognition technology that is finely tuned to the nuances of medical terminology and diverse accents found in the medical environment. * Entity Recognition: Beyond simple transcription, this model is equipped with sophisticated Natural Language Processing (NLP) capabilities. It automatically identifies and categorizes key medical entities such as symptoms, diagnoses, medications, and dosages in the transcribed text.
- **Detected entities:** Admission_Discharge, Age, Alcohol, Allergen, BMI, Birth_Entity, Blood_Pressure, Cerebrovascular_Disease, Date, Diabetes, Diet, Direction, Disease_Syndrome_Disorder, Dosage, Drug_BrandName, Drug_Ingredient, Duration, EKG_Findings, Employment, Form, Frequency, Gender, HDL, Heart_Disease, Height, Hyperlipidemia, Hypertension, ImagingFindings, Imaging_Technique, Procedure, Pulse, Race_Ethnicity, Relationship_Status, RelativeDate, RelativeTime, Respiration, Route, Smoking,etc.
- **Assertion status**: Each identified entity is qualified as Hypothetical, Someoneelse, Past, Absent, Family, Planned, Possible or Present. **Relations**: The model detects three relations: is_finding_of, is_date_of, is_result_of
Details
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Latest version
Deployed on AWS
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Pricing
Free trial
Try this product free for 15 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m4.4xlarge Inference (Batch) Recommended | Model inference on the ml.m4.4xlarge instance type, batch mode | $95.04 |
ml.m4.4xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.4xlarge instance type, real-time mode | $95.04 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $47.52 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $213.84 |
ml.m4.xlarge Inference (Batch) | Model inference on the ml.m4.xlarge instance type, batch mode | $23.76 |
ml.c5.4xlarge Inference (Batch) | Model inference on the ml.c5.4xlarge instance type, batch mode | $95.04 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $47.52 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $47.52 |
ml.c5.9xlarge Inference (Real-Time) | Model inference on the ml.c5.9xlarge instance type, real-time mode | $213.84 |
ml.m4.xlarge Inference (Real-Time) | Model inference on the ml.m4.xlarge instance type, real-time mode | $95.04 |
Vendor refund policy
No refunds are possible.
Legal
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
New version with updated output parameters.
Additional details
Inputs
- Summary
Supported audio file formats include: mp3, wav, aac, m4a.
- Limitations for input type
- 11MB
- Input MIME type
- application/octet-stream
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/speech_to_text_medical/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/speech_to_text_medical/inputs/batch
Resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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