Sold by: John Snow Labs
Deployed on AWS
Free Trial
This model is designed to map drugs to RxNorm codes and their associated drug classes.
Overview
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.
Details
Sold by
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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 based on the AWS instance you run, plus the processing mode you choose. Batch mode handles large sets of text in scheduled jobs. Real-time mode returns RxNorm codes on demand. Batch options run on the 2xlarge size across the m, c, and r instance families. Real-time options run on the xlarge size across the same families. The m family balances compute and memory, c favors compute, and r favors memory. Newer generations (6i, 7i) sit alongside older ones (m4, c5, r5). Your total cost scales with hours used and instance choice.
Top-of-mind questions for buyers
One HostHrs unit is one hour that your chosen instance runs the model. You pay for each hour the instance is active. Partial hours and the specific instance type you select determine the rate. Running more instances or longer sessions raises the total hours billed.
Software charges meter running time per host hour. When the instance is stopped, software charges stop too. You pay only for the hours the instance is active and processing. Note that underlying AWS infrastructure fees may still apply for stored resources.
Batch mode runs on 2xlarge instances and processes large sets of text in scheduled jobs. Real-time mode runs on xlarge instances and returns RxNorm codes on demand. Both bill per host hour. Batch suits bulk processing; real-time suits live lookups as requests arrive.
nlp.johnsnowlabs.com
Helpful?
Vendor refund policy
No refunds are possible.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Latest dataset update.
Additional details
Inputs
- Summary
To use the model, you need to provide input in one of the following supported formats:
JSON Format
Provide input as JSON. We support two variations within this format:
1. 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", ... ] }
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
- text/csv, 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.
Similar products
The SNOMED Clinical Terminology Mapper pipeline is designed to extract and normalize clinical entities from unstructured medical text.
It identifies a wide range of clinical entities and maps them to their corresponding SNOMED codes .
This facilitates standardized data representation, enabling efficient clinical data analysis and interoperability.
Extracts Test entities from clinical texts and maps them to their corresponding LOINC codes .
This pipeline can extract clinical conditions, and map the clinical conditions to their respective ICD-10-CM codes.
Pivot is a healthcare interoperability and data quality solution that combines format transformation, terminology normalization, and data quality validation into a single application.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.