Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Healthcare Analytics Made Simple
  • Toc
  • feedback
Healthcare Analytics Made Simple

Healthcare Analytics Made Simple

By : Kumar, Khader
4.4 (8)
close
Healthcare Analytics Made Simple

Healthcare Analytics Made Simple

4.4 (8)
By: Kumar, Khader

Overview of this book

In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples.
Table of Contents (11 chapters)
close

Making the response variable

In some cases, the response variable that we are trying to predict may already be a separate well-defined column. In those cases, simply converting the response from a string to a numeric type before splitting the data into train and test sets will suffice.

In our specific modeling task, we are trying to predict which patients presenting to the ED will eventually be hospitalized. In our case, hospitalization encompasses:

  • Those admitted to an inpatient ward for further evaluation and treatment
  • Those transferred to a different hospital (either psychiatric or non-psychiatric) for further treatment
  • Those admitted to the observation unit for further evaluation (whether they are eventually admitted or discharged after their observation unit stay)

Accordingly, we must do some data wrangling to assemble all of these various outcomes into a single response...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete