Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
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Keywords: COVID-19, Medical Imaging, Deep Learning, PACS Viewer, and HealthShare.
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.
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.
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.
This is my introduction to a series of posts explaining how to create an end-to-end Machine Learning system.
Starting with one problem
Our IRIS Development Community has several posts without tags or wrong tagged. As the posts keep growing the organization
of each tag and the experience of any community member browsing the subjects tends to decrease.
First solutions in mind
We can think some usual solutions for this scenario, like: