Sold by: John Snow Labs
Deployed on AWS
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Extracts medical entities from clinical texts maps these entities to corresponding RxNorm Concept Unique Identifier (RxCUI) codes
Overview
Extracts medical entities from clinical texts and map these entities to their corresponding RxNorm Concept Unique Identifier (RxCUI) codes.
This model was created to facilitate the accurate mapping of drugs to their corresponding RxNorm codes and related drug classes. It is an essential tool for healthcare professionals and pharmacists, ensuring precise medication identification and categorization, which is crucial for patient safety, medication management, and healthcare interoperability.
Key Features:
The model accurately maps various drug names, including brand and generic names, to their respective RxNorm codes. RxNorm, developed by the National Library of Medicine, provides standardized nomenclature for medications, aiding in clear and consistent drug identification.
In addition to mapping drugs to RxNorm codes, the model identifies the related RxNorm drug class, providing essential information about the pharmacological classification of each medication. This feature is useful for understanding the therapeutic uses and mechanisms of action of different drugs. The model can interpret a wide range of drug-related terminology from diverse sources, including electronic health records, prescription data, and pharmacological literature.
By providing accurate drug classifications, the model assists healthcare professionals in better understanding drug interactions, contraindications, and appropriate medication regimens, enhancing patient care and safety. This model can be used in pharmacy management to simplify medication dispensing and inventory management through the provision of standardized drug information. It can also be used to enhance Electronic Health Records (EHR) systems by incorporating precise drug coding, resulting in improved medication reconciliation and clinical decision support. Furthermore, it can assist in healthcare data analytics by facilitating the analysis of medication data for research purposes, policy-making, and the improvement of healthcare quality.
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 * All the concept class IDs for the al resolutions. * Codes of all resolutions * Resolution of the NER chunk and the ground truth of the resolution code. * All the cosine distance scores of the for all resolutions
- Process up to 3 M chars per hour in real-time and 20 M chars per hour in batch mode.
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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 |
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 model, with no upfront commitment. Pricing splits into two usage modes: batch inference, for processing large volumes at once, and real-time inference, for on-demand queries. Within each mode, you choose from several instance types across general-purpose (m-series), compute-optimized (c-series), and memory-optimized (r-series) families. Batch options use larger 2xlarge sizes, while real-time options use xlarge sizes. Your hourly rate depends on the instance family, generation, and size you select. You run and are billed for one instance at a time.
Top-of-mind questions for buyers
One host hour is one hour that a chosen instance runs the model. The model maps medical drug text to RxNorm codes, extracting entities from clinical text and matching them to standard concept identifiers. You are billed per hour that instance stays active.
Charges apply per hour while the instance runs. A stopped instance does not accrue software host-hour charges. Underlying AWS infrastructure fees, such as storage, may still apply separately even when the model is not actively processing. The software meters running time only.
Both meter host hours on the running instance. Batch mode runs on 2xlarge instances to process large document volumes in one job. Real-time mode runs on xlarge instances to answer on-demand queries. You pick one mode and instance, and pay for the hours it runs.
nlp.johnsnowlabs.com
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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 to johnsnowlabs_version: 6.4.0 Spark-NLP==6.4.0 Spark-Healthcare==6.4.0
Additional details
Inputs
- Summary
JSON 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/rxnorm_vdb_resolver/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/rxnorm_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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