Sold by: John Snow LabsÂ
Deployed on AWS
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Classify health-related text in colloquial language according to the presence or absence of mentions of drug side effects.
Overview
This model is specialized in the classification of health-related textual data, particularly focusing on colloquial expressions. Its core functionality is to accurately identify whether the text includes references to side effects stemming from drug usage. This capability is crucial for monitoring and analyzing patient feedback, social media discussions, and informal patient-reported outcomes that are often expressed in non-technical language.
Leveraging state-of-the-art machine learning algorithms, the model is adept at parsing the nuances of everyday language used by individuals when describing their experiences with medications. It has undergone extensive training and fine-tuning on a diverse dataset comprising medical forums, patient testimonials, and other sources of informal health-related discourse. This ensures the model's effectiveness in recognizing a wide array of vernacular expressions and idioms pertaining to side effects.
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
- This model can be used across various domains within the healthcare sector, including pharmacovigilance, drug safety monitoring, and patient care improvement initiatives. It enables stakeholders to harness the power of unstructured text data, transforming it into actionable insights regarding drug safety and efficacy. Healthcare professionals and organizations can proactively address patient concerns, enhance drug safety protocols and contribute to the overall improvement of healthcare delivery.
- This model is particularly valuable for organizations looking to integrate advanced NLP capabilities into their healthcare analytics tools, patient feedback systems, and drug safety monitoring frameworks.
Details
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Delivery method
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.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $9.84 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $9.84 |
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No refunds are possible.
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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
This model is trained to classify health-related text in colloquial language according to the presence or absence of mentions of side effects related to drugs.
Additional details
Inputs
- Summary
To use the model for text prediction, you need to provide input in one of the following supported formats:
- Single Text Document Provide a single text document as a string.
{ "text": "Single text document" }
- Array of Text Documents Use an array containing multiple text documents. Each element represents a separate text document. { "text": [ "Text document 1", "Text document 2", ] }
- Input MIME type
- application/json
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.classify.bert_sequence.vop_drug_side_effect.pipeline/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.classify.bert_sequence.vop_drug_side_effect.pipeline/inputs/batch
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
text | Contains the text to analyze. | Type: FreeText | Yes |
Resources
Vendor 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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