Sold by: John Snow LabsÂ
Deployed on AWS
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Detects adverse reactions of drugs (ADE) from reviews, tweets, and medical text.
Overview
This model is engineered for the extraction of adverse drug events (ADEs) from unstructured clinical texts, leveraging several components finely tuned for this purpose:
- Entity Recognition: Initially, the model accurately identifies entities related to adverse events (such as rash, nausea) and drug.
- Assertion Status Detection: Subsequently, it assigns an assertion status (e.g., present, negated, historical, hypothetical) to each identified ADE entity, taking into account the surrounding context.
- Document Classification: The model then classifies the entire document, discerning whether it contains a report of an ADE. This classification aids in filtering documents more likely to possess relevant ADE information. Covered classes: ADE, noADE
- Relation Extraction: detection of relationships between the extracted ADE entities and drug entities. Covered relations: 0 (absent), 1 (present).
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 analyzes up to 1.4M chars/hour in real time and 3.5M in batch to detect adverse drug reactions (ADRs). It identifies side effects, links drugs to reactions, and determines if events are drug-induced, supporting safer care and informed decision-making.
- In the digital era, patients frequently share medication experiences across various platforms like online reviews, social media, and forums. This model meticulously scans such diverse content, including clinical notes, to detect drug-associated adverse reactions. It serves as a proactive tool for early ADR identification, thereby enhancing patient safety and refining drug prescription practices.
- Covered entities: DRUG, ADE. Covered assertion statuses: absent, present, conditional, associated_with_someone_else, hypothetical, possible. Relations: 0 (absent), 1 (present). Covered classes: ADE, noADE
Details
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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.m4.2xlarge Inference (Batch) Recommended | Model inference on the ml.m4.2xlarge instance type, batch mode | $47.52 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $23.76 |
Vendor refund policy
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
Model optimization
Additional details
Inputs
- Summary
Input Format
- Single Text Document { "text": "Single text document" }
- Array of Text Documents { "text": [ "Text document 1", "Text document 2", ... ] }
- JSON Lines (JSONL) Format {"text": "Text document 1"} {"text": "Text document 2"}
- Input MIME type
- application/json, application/jsonlines
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.explain_doc.clinical_ade/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/en.explain_doc.clinical_ade/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 | The text to be analyzed. | 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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