Sold by: John Snow Labs
Deployed on AWS
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Extracts Test entities from clinical texts and maps them to their corresponding LOINC codes .
Overview
This pipeline extracts Test entities from clinical texts and maps them to their corresponding Logical Observation Identifiers Names and Codes (LOINC) codes.
It is pivotal for enhancing interoperability in healthcare systems, ensuring accurate and consistent data across various platforms.
The model was trained on an augmented version of a dataset previously used in LOINC resolver models. This enhanced dataset includes a diverse range of clinical terminologies and corresponding LOINC codes, ensuring comprehensive coverage and versatility in mapping capabilities.
This model is indispensable for laboratories, electronic health record (EHR) systems, and healthcare data analytics platforms. It streamlines the process of standardizing laboratory data, ensuring compliance with healthcare regulations and facilitating seamless data exchange across different healthcare systems.
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
- Simply pass in one or more text documents and get back : - Detected Named Entity Recognition (NER) chunk - NER chunk Position, Label and Confidence Score - Resolution and Resolution code - Cosine distance score of the resolution - All the other possible resolutions of the NER chunk - Codes of all resolutions - Resolution and ground truth of the resolution code - Domain(s) associated with the resolution. - All the cosine distance scores of the for all resolutions
- Process up to 3 M chars per hour in real-time and 10 M chars per hour in batch mode.
Details
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Pricing
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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 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $47.52 |
ml.m6i.2xlarge Inference (Batch) | Model inference on the ml.m6i.2xlarge instance type, batch mode | $47.52 |
ml.m7i.2xlarge Inference (Batch) | Model inference on the ml.m7i.2xlarge instance type, batch mode | $47.52 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $47.52 |
ml.c6i.2xlarge Inference (Batch) | Model inference on the ml.c6i.2xlarge instance type, batch mode | $47.52 |
ml.c7i.2xlarge Inference (Batch) | Model inference on the ml.c7i.2xlarge instance type, batch mode | $47.52 |
ml.r6i.2xlarge Inference (Batch) | Model inference on the ml.r6i.2xlarge instance type, batch mode | $47.52 |
ml.r7i.2xlarge Inference (Batch) | Model inference on the ml.r7i.2xlarge instance type, batch mode | $47.52 |
Dimensions summary
You pay by the hour for the compute instance that runs the terminology mapping model. Charges follow usage, so the total scales with how many hours each instance runs. Two inference modes set the structure. Batch mode handles large volumes of documents at once and runs on 2xlarge instances. Real-time mode returns codes on demand and runs on xlarge instances. Within each mode, you choose from several instance families offering different balances of general-purpose, compute, and memory capacity. Larger or newer instance types carry higher hourly rates. You select the instance that fits your throughput and performance needs.
Top-of-mind questions for buyers
One HostHrs unit is one hour that the chosen instance runs the mapping model. Charges accrue while the instance is running. Fully stopped instances do not accrue software charges, though underlying AWS storage fees may still apply. You control cost by starting and stopping instances around your workload.
Batch mode runs on 2xlarge instances and processes large document volumes in scheduled runs. Real-time mode runs on xlarge instances and returns codes on demand for live queries. You pick the mode that matches your workload, and charges meter the running hours of that instance only.
No. Charges meter running instance-hours, not the number of documents, characters, or code lookups. You can process any volume during a paid hour. Actual throughput depends on the instance capacity you select, since faster or higher-memory instances handle more work per hour.
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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
Updated internal libs to 6.4.0
Additional details
Inputs
- Summary
JSON Format Provide input as JSON. We support two variations within this format:
- 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", ... ] }
- Single Text Document: Provide a single text document as a string.
{ "text": "Single text document" }
JSON Lines (JSONL) Format
Provide input in JSON Lines format, where each line is a JSON object representing a text document.
{"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/loinc_vdb_resolver/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/loinc_vdb_resolver/inputs/batch
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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