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Steve Martinelli edited this page Apr 19, 2018 · 5 revisions

Short Name

Classify ICD10 data with IBM Watson Natural Language Classifier

Short Description

A simple web app that shows how Watson's Natural Language Classifier (NLC) can classify ICD-10 code.

Offering Type

Cognitive

Introduction

This application was built to demonstrate IBM's Watson Natural Language Classifier (NLC). It uses the Watson Python SDK to create the classifier, list classifiers, and classify the input text. We also make use of the freely available ICD-10 API which, given an ICD-10 code, returns a name and description.

Author

By Steve Martinelli and Scott D'Angelo

Code

Demo

  • N/A

Video

Overview

This application was built to demonstrate IBM's Watson Natural Language Classifier (NLC). The data set we will be using, ICD-10-GT-AA.csv, contains a subset of ICD-10 entries. ICD-10 is the 10th revision of the International Statistical Classification of Diseases and Related Health Problems. In short, it is a medical classification list by the World Health Organization (WHO) that contains codes for: diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. Hospitals and insurance companies alike could save time and money by leveraging Watson to properly tag the most accurate ICD-10 codes.

When the reader has completed this pattern, they will understand how to:

  • Create a Natural Language Classifier (NLC) service and use it in a Python application.
  • Train a NLC model using csv data.
  • Deploy a web app with Flask to allow the NLC model to be queried.
  • Quickly get a classification of a disease or health issue using the Natural Language Classifier trained model.

Flow

  1. CSV files are sent to the Natural Language Classifier service to train the model.
  2. The user interacts with the web app UI running either locally or in the cloud.
  3. The application sends the user's input to the Natural Language Classifier model to be classified.
  4. The information containing the classification is returned to the web app.

Included Components

Featured Technologies

  • Artificial Intelligence: Artificial intelligence can be applied to disparate solution spaces to deliver disruptive technologies.
  • Cloud: Accessing computer and information technology resources through the Internet.
  • Python: Python is a programming language that lets you work more quickly and integrate your systems more effectively.

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