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Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

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Extract entities from patient narratives Free trial

By:
John Snow Labs
Extract demographic entities, substance abuse, psychological conditions and more from social media published patient narratives.
  • This product has been removed and is no longer available to new customers.

Product Overview

This model is specialized on health-related text analysis in colloquial language within the domain of Public Health and Voice of Patients. It is designed to identify and extract various entities such as Diagnosis, Treatments, Tests, Psychological Conditions, Relationship Status, Symptoms, Procedures, Health Status, Treatments, Substances etc. , informally presented in patient narratives. This model is tailored for gleaning crucial insights directly from patient narratives, proficiently identifying entities like gender, age, substance abuse, psychological conditions, and many more. Engineered with the healthcare provider in mind, it ensures accurate extraction of patient-expressed data points from social media and on-line sources. By leveraging this pipeline, medical professionals can gain a more comprehensive understanding of the patient experience, ensuring care that is both patient-centered and data-informed.

Key Data

By
John Snow Labs
Categories
ML Solutions
Names Entity Recognition - NER
Healthcare & Life Sciences
Type
Model Package
Fulfillment Methods
Amazon SageMaker

Highlights

  • Extracted entities: Gender, Employment, Age, BodyPart, Substance, Form, PsychologicalCondition, Vaccine, Drug, DateTime, ClinicalDept, Laterality, Test, AdmissionDischarge, Disease, VitalTest, Dosage, Duration, RelationshipStatus, Route, Allergen, Frequency, Symptom, Procedure, HealthStatus, InjuryOrPoisoning, Modifier, Treatment, SubstanceQuantity, MedicalDevice, TestResult

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Usage Information

Model input and output details

Input

Summary

To use the model for text prediction, you need to provide input in one of the following supported formats:

  1. Single Text Document Provide a single text document as a string.

{ "text": "Single text document" }

  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",
    ] }
Input MIME type
application/json
Sample input data
Expand all input descriptions
Field name
text
Description
Contains the text to analyze.
Constraints

Type: FreeText

Required
Yes

Output

Summary

The model generates output in the following json format:

{ "predictions": [ { "document": "Text of the document 1", "ner_chunk": "Named Entity 1", "begin": Start Index, "end": End Index, "ner_label": "Label 1", "confidence": Score } ... ] }

Output MIME type
application/json
Sample output data
Expand all output descriptions
Field name
predictions
Description
An array containing predictions for each input document. Type: FreeText
Always returned
Yes
Data type
Field name
document
Description
The original input text for which predictions are made. Type: FreeText
Always returned
Yes
Data type
Field name
ner_chunk
Description
Named entity recognized in the document. Type: FreeText
Always returned
Yes
Data type
Field name
begin
Description
Starting character index of the named entity chunk within the document. Type: Integer
Always returned
Yes
Data type
Field name
end
Description
Ending character index of the named entity chunk within the document. Type: Integer
Always returned
Yes
Data type
Field name
ner_label
Description
Label assigned to the named entity. Type: Categorical Allowed values: Gender, Employment, Age, BodyPart, Substance, Form, PsychologicalCondition, Vaccine, Drug, DateTime, ClinicalDept, Laterality, Test, AdmissionDischarge, Disease, VitalTest, Dosage, Duration, RelationshipStatus, Route, Allergen, Frequency, Symptom, Procedure, HealthStatus, InjuryOrPoisoning, Modifier, Treatment, SubstanceQuantity, MedicalDevice, TestResult
Always returned
Yes
Data type
Field name
confidence
Description
Confidence score associated with the prediction. Type: Continuous Minimum: 0 Maximum: 1
Always returned
Yes
Data type

Sample notebook

Additional Resources

End User License Agreement

By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

Support Information

Extract entities from patient narratives

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Refund Policy

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