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Healthcare Analytics Made Simple
Healthcare Analytics Made Simple

Healthcare Analytics Made Simple: Techniques in healthcare computing using machine learning and Python

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Healthcare Analytics Made Simple

Healthcare Foundations

This chapter is mainly aimed at developers who have limited experience of healthcare. By the end of it, you will be able to describe basic characteristics of healthcare delivery in the United States, you will be familiar with specific legislation in the US that is relevant to analytics, you will understand how data in healthcare is structured, organized, and coded, and you will be aware of frameworks for thinking about analytics in healthcare.

Healthcare delivery in the US

The healthcare industry impacts all of us, through its interactions with ourselves, our loved ones, our family, and our friends. The high costs associated with the healthcare industry are intertwined with the physical, emotional, and spiritual trauma that occurs when someone close to us becomes ill or feels pain.

In the United States, the healthcare system is in a fragile state, as healthcare expenditure exceeds 15% of the nation's total GDP; this proportion far exceeds that of other developed countries, and is expected to rise to at least 20% by the year 2040 (Braunstein, 2014; Bernaert, 2015). The rise in healthcare costs in the US, and internationally, can be attributed to several factors. One is a shift in demographics to a more elderly population. Average life expectancy (LE) rose to in excess of 80 years of age for the first time in 2011...

Patient data – the journey from patient to computer

The clinical data collection process starts when a patient starts telling a physician about his or her condition. This is known as the patient history, and since it is not observed directly by the physician, but instead recounted by the patient, the patient’s story is known as subjective information. In contrast, objective information comes from the physician and consists of the physician's own observations about the patient, from the physical examination, lab tests, and imaging studies, to other diagnostic procedures. Together, the subjective and objective information makes up the clinical note.

There are several types of clinical notes used in healthcare. The history and physical (H&P) is the most thorough and comprehensive clinical note. It is usually obtained when an outpatient physician sees a patient...

Standardized clinical codesets

Being philosophical for a moment, every known object that has a significant importance attributed to it has a name. The organs you are using to read these words are known as eyes. The words are written on pieces of paper called pages. To turn the pages, you use your hands. These are all objects that we have named so that we can identify them easily.

In healthcare, important entities—diseases, procedures, lab tests, drugs, symptoms, bacteria species, for example, have names and identities too. For example, the failure of the heart valves to pump blood to the rest of the body is known as heart failure. ACE inhibitors are a class of drugs used to treat heart failure.

A problem arises, however, when healthcare industry workers associate the same entity with different identities. For example, one physician may refer to "heart failure&quot...

Breaking down healthcare analytics

So you've decided to enter the world of analytics, and you know you want to focus on the healthcare industry. However, that barely narrows down the problem space, as there are hundreds of open problems in healthcare that are being addressed with machine learning and other analytical tools. If you have ever typed the words "machine learning in healthcare" into Google or PubMed, you have probably discovered how vast the ocean of machine learning use cases in healthcare is. In academia, publications focus on problems ranging from predicting dementia onset in the elderly to predicting the occurrence of a heart attack within six months to predicting which antidepressants patients will best respond to. How do you pick the problem on which to focus? This section is all about answering that question. Choosing the appropriate problem to...

Summary

In Chapter 1, Introduction to Healthcare Analytics, we introduced the Healthcare Analytics triumvirate of healthcare, mathematics, and computer science. In this chapter, we have looked at some foundational healthcare topics. In Chapter 3, Machine Learning Foundations, we will look at some of the mathematical and machine learning concepts that underlie healthcare analytics.

References and further reading

Bernaert, Arnaud (2015). "Five Global Health Trends You Can't Ignore." UPS Longitudes. April 13, 2015. longitudes.ups.com/five-global-health-trends-you-cant-ignore/.

Braunstein, Mark (2014). Contemporary Health Informatics. Chicago, IL: AHIMA Press.

Esfandiari N, Babavalian MR, Moghadam A-ME, Tabar VK (2014) Knowledge discovery in medicine: current issue and future trend. Expert Syst Appl 41(9): 4434–4463.

Martin, GJ (2005). "Screening and Prevention of Disease." In Kasper DL, Braunwald E, Fauci AS, Hauser SL, Longo DL, Jameson JL. eds. Harrison's Principles of Internal Medicine, 16e. New York, NY: McGraw-Hill.

OECD (2013), Health at a Glance 2013: OECD Indicators, OECD Publishing. http://dx.doi.org/10.1787/health_glance-2013-en.

Smith, Robert C (1996). The Patient's Story. Boston, MA: Little, Brown.

US Department...

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Key benefits

  • Perform healthcare analytics with Python and SQL
  • Build predictive models on real healthcare data with pandas and scikit-learn
  • Use analytics to improve healthcare performance

Description

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.

Who is this book for?

Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.

What you will learn

  • Gain valuable insight into healthcare incentives, finances, and legislation
  • Discover the connection between machine learning and healthcare processes
  • Use SQL and Python to analyze data
  • Measure healthcare quality and provider performance
  • Identify features and attributes to build successful healthcare models
  • Build predictive models using real-world healthcare data
  • Become an expert in predictive modeling with structured clinical data
  • See what lies ahead for healthcare analytics

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 31, 2018
Length: 268 pages
Edition : 1st
Language : English
ISBN-13 : 9781787283220
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Product Details

Publication date : Jul 31, 2018
Length: 268 pages
Edition : 1st
Language : English
ISBN-13 : 9781787283220
Category :
Languages :
Concepts :

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Table of Contents

10 Chapters
Introduction to Healthcare Analytics Chevron down icon Chevron up icon
Healthcare Foundations Chevron down icon Chevron up icon
Machine Learning Foundations Chevron down icon Chevron up icon
Computing Foundations – Databases Chevron down icon Chevron up icon
Computing Foundations – Introduction to Python Chevron down icon Chevron up icon
Measuring Healthcare Quality Chevron down icon Chevron up icon
Making Predictive Models in Healthcare Chevron down icon Chevron up icon
Healthcare Predictive Models – A Review Chevron down icon Chevron up icon
The Future – Healthcare and Emerging Technologies Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(8 Ratings)
5 star 75%
4 star 0%
3 star 12.5%
2 star 12.5%
1 star 0%
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J. Weeks May 19, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great overview of several broad topics. The book was exceptionally well written in an easy to read and referencable manner. I would highly recommend the book to anyone new to the healthcare industry or interested in learning advanced analytics with Python.
Amazon Verified review Amazon
M. D. Reader Mar 17, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
What the book does well: I thought the most useful part of the book was an overview of the United States Healthcare system and how healthcare has changed after the Affordable Care Act. This includes Meaningful Use for electronic health records and accountable care organizations. The book is also good for some terminology related to medical claims and payer data such as International Statistical Classification of Diseases (ICD) ICD10 diagnosis codes, and Current Procedural Terminology (CPT) for medical procedures.What could have been better: There is very little Python code in a book advertised as being about machine learning and Python programming. The few chapters that do contain Python code have some pretty trivial examples, like calculating the means and counts by group of a dataset using Pandas and NumPy, and training a very basic linear regression classifier with scikit-learn on a tiny dataset. The only real-world data given as an example is the Hospital Ambulatory Medical Care Survey (NHAMCS) from the CDC’s website. Chances are if you’re doing any kind of healthcare data analytics you will be working with medical claims data and clinical data from an electronic health record and I wish the book had given more examples using these types of data. Also, healthcare data gets messy pretty quickly, some examples and strategies on how to assure data quality would have been appreciated.It’s an okay book about healthcare data and the United States healthcare industry. You might be better served by a book specifically about machine learning and applying it to healthcare data.
Amazon Verified review Amazon
SeattleAndrew Mar 01, 2020
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
This book is long on talking about healthcare analytics which it should t need, vs what it promoted itself as an example for code that was healthcare based.I feel like many of chapters Are refreshers on basics vs in depth work on algorithms and founds on probability how much specifically applies to healthcare issues.I feel like of the 100 page book only about 5 to 10 pages are of value, not even that much value.I just dropped the review down another star as I was really looking for ward to a complex “made simple” analytics read about application of healthcare.Instead of the basics refresher course on course versus advanced healthcare.Which is the name of the book, “blah blah blah made simple” which is obvious to every industry, a very tiny portion is specific to only healthcare.This book should be republished 2nd edition as 10 page pamphlet on healthcare analytics, the rest could be found on a five to ten minute search of LinkedIn for paper saving sake.
Amazon Verified review Amazon
Aalap Shah, MD Jan 17, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As an anesthesiologist, I have found that our specialty has shifted from traditional experience and bench/lab research to data-drive approaches in optimizing our patients' outcomes. As more patients enter the US hospital systems, more and more data points are being combined to help establish trends and extrapolate future results.While I personally have limited experience with data mining at different institutions (e.g. SQL), this primer helps even the least computer-savvy person read between the lines of the data-heavy EHR. One can understand how data points can start to be manipulated for research or quality/process improvement purposes. The primer does a great job of address important and trendy topics in healthcare quality improvement by empowering the reader with the tools to create predictive models for outcomes, code and analyze diagnoses and sub-groups, and plot data in a way that C-Suite and physicians alike can appreciate and understand.Overall, a very thorough primer that flows well between the tenets of knowledge germane to medical specialties and helps to create a dialog between non-programming physicians and analysts seeking to improve the healthcare system.
Amazon Verified review Amazon
DI Jul 07, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent book to learn and to apply ML in healthcare.It s very well writen, so everybody can understand. I used this book as guideline for my project.
Amazon Verified review Amazon
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