InterSystems IntegratedML is an all-SQL machine learning (ML) module for InterSystems IRIS or IRIS for Health that: - Gives users the ability to create, train and deploy powerful models from simple SQL syntax without requiring data scientists. - Wraps "best of breed" open source and proprietary "AutoML" frameworks including DataRobot. - Focuses on easy deployment to IRIS, so you can easily add machine learning to your applications.
Taking advantage of the Quiniela ML application and as we indicated in the previous article, we are going to explain how we can perform a JWT authentication between our frontend developed in Angular and our backend developed in InterSystems IRIS.
I remind you of the architecture of our QuinielaML project:
Recently @Anastasia Dyubaylopublished a post (this one) showing a new IntegratedML functionality for time series predictions that @Thomas Dyaralready presented to us at the Global Summit 2023 so, let's go to set up a small workshop to test it!
We continue with the series of articles related to the QuinielaML application. In this article we are going to cover how to prepare the raw data that we have captured using the Embedded Python functionality.
Welcome dear members of the Community to the presentation and first article of a small project that will demonstrate the capabilities of InterSystems IRIS to provide full backup functionality for a web application developed in Angular. This article will be limited to presenting the concept as well as the InterSystems IRIS functionalities used in a general way, going into more detail in subsequent articles.
A simple data analysis example created in IntegratedML and Dashboard
Based on InterSystems' Integrated ML technology and Dashboard, automatically generate relevant predictions and BI pages based on uploaded CSV files. The front and back ends are completed in Vue and Iris, allowing users to generate their desired data prediction and analysis pages with simple operations and make decisions based on them.
As you know, if you regularly read the articles that are published in the Community, last May InterSystems organized the JOnTheBeach2023 Hackathon held in Malaga (Spain). The topic that was proposed was the use of predictive analysis tools that InterSystems IRIS makes available to all developers with IntegratedML.
The platform server entry configuration prediction application connects to Iris in Java and uses its Integrated ML technology to analyze data such as hospital outpatient volume, number of services, number of messages, and message save time. It can predict the server configuration required for the hospital entry platform before the hospital integration platform enters, providing convenience for customers.
In recent years, artificial intelligence technologies for text generation have developed significantly. For example, text generation models based on neural networks can produce texts that are almost indistinguishable from texts written by humans. ChatGPT is one such service. It is a huge neural network trained on a large number of texts, which can generate texts on various topics and be matched to a given context.
The Customer Churn Predictor application is a tool that demonstrates how to use IntegratedML to helps companies identify which customers are likely to leave in the near future and develop customer retention strategies. Benefits of the similar application include:
IntegratedML is a feature helps us and our teams easily implement machine learning (ML) without dedicated ML experts and data scientists. If you do not need particularly complex ML function, integratedML is a good choice and convenient that only requires executing 3 SQL queries to build predictive models directly from InterSystems IRIS to ML engine.
It is very interesting to compare different BI technologies. It is curious to me what the differences are in functionality, development tools, speed and usability.
Maternal Risk can be measured from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Yasir Hussein Shakir published a very useful dataset for training ML algorithms in the detection/prediction of Maternal Risk.
Kidney Disease can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Akshay Singh published a very useful dataset for training ML algorithms in the detection/prediction of kidney disease. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/akshayksingh/kidney-disease-dataset.
Diabetes can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the National Institute of Diabetes and Digestive and Kidney Diseases published a very useful dataset for training ML algorithms in the detection/prediction of diabetes. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/mathchi/diabetes-data-set.
Prediction is a critical to the Maternal healthcare. The Health Dataset Application (https://openexchange.intersystems.com/package/Health-Dataset) has 10 real health datasets to predict the most important diseases and health problems, including Maternal Risk.
This article detail the steps to predict Maternal Risk using the InterSystems IRIS IntegratedML. This is a technology of InterSystems to do predictions using SQL Commnands! Great!
Have you tried the InterSystems learning platform lab for IRIS IntegratedML? In that lab you can train and test a model on a readmission dataset and be able to predict when a patient will be readmitted or not, or calculate its probability of being readmitted.
You can try it without any installation on your system, all you have to do is start a virtual lab environment (Zeppelin) and play it around!
Recently I noticed a Kaggle dataset for the prediction of whether a Covid-19 patient will be admitted to ICU. It is a spreadsheet of 1925 encounter records of 231 columns of vital signs and observations, with the last column of "ICU" being 1 for Yes or 0 for No. The task is to predict whether a patient will be admitted to ICU based on known data.
Continued from the previous Part I ... In part I, we walked through traditional ML approaches on this Covid-19 dataset on Kaggle.
In this Part II, let's run the same data & task, in its simplest possible form, through IRIS integratedML which is a nice & sleek SQL interface for backend AutoML options. It uses the same environment.
Currently, the process of using machine learning is difficult and requires excessive consumption of data scientist services. AutoML technology was created to assist organizations in reducing this complexity and the dependence on specialized ML personnel.
AutoML allows the user to point to a data set, select the subject of interest (feature) and set the variables that affect the subject (labels). From there, the user informs the model name and then creates his predictive or data classification model based on machine learning.
This is the second post of a series explaining how to create an end-to-end Machine Learning system.
Exploring Data
The Intersystem IRIS already has what we need to explore the data: an SQL Engine! For people who used to explore data in
csv or text files this could help to accelerate this step. Basically we explore all the data to understand the intersection
(joins) which should help to create a dataset prepared to be used by a machine learning algorithm.
Following up the previous part, it's time to take advantages for IntegratedML VALIDATION MODEL statement, to provide information in order to monitor your ML models. You can watch it in action here
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Open Exchange app
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