Machine learning (ML) is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" with data, without being explicitly programmed.
This is another 5-minute simple note on invoking the IRIS JDBC driver via Python 3 within i.e. a Jupyter Notebook, to read from and write data into an IRIS database instance via SQL syntax, for demo purpose.
See how InterSystems IRIS Adaptive Analytics can be used to aggregate and query billions of records from a virtual cube, applying machine learning and analytics to that data.
Keywords: COVID-19, Medical Imaging, Deep Learning, PACS Viewer, and HealthShare.
Purpose
We are all gripped by this unprecedented Covid-19 pandemic. While supporting our customers in battlefields by any means, we also observed various fighting fronts against Covid-19 by leveraging today's AI powers.
i want to restrict certain IPs (ip for now and looking for more such parameters) from running my URL for security purposes...
i wanted to know how to access their ips and compare them with the list of restricted IPs, also if not IPs are there any other unique browser parameters which can be used for access control? and enhance security
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.
NLP stands for Natural Language Processing which is a field of Artificial Intelligence with a lot of complexity and
techniques to in short words "understand what are you talking about".
And FHIR is...???
FHIR stands for Fast Healthcare Interoperability Resources and is a standard to data structures for healthcare. There are
some good articles here explainig better how FHIR interact with Intersystems IRIS.
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.
ML Toolkit for InterSystems IRIS, besides Python/R/Julia, allows orchestrating cloud-based advanced analytics services, such as Microsoft Azure Data Factory and Machine Learning:
This is the third post of a series explaining how to create an end-to-end Machine Learning system.
Training a Machine Learning Model
When you work with machine learning is common to hear this work: training. Do you what training mean in a ML Pipeline?
Training could mean all the development process of a machine learning model OR the specific point in all development process
that uses training data and results in a machine learning model.
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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