Healthcare analytics made simple : techniques in healthcare computing using machine learning and Python / Vikas Kumar.

Format:

Book

Author/Creator:

Kumar, Vikas, author.

Language:

English

Subjects (All):

Medical care--Data processing.

Medical care.

Machine learning.

Python (Computer program language).

Physical Description:

1 online resource (258 pages)

Edition:

1st edition

Place of Publication:

Birmingham, England : Packt, 2018.

System Details:

text file

Summary:

Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features 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 Book 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. 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 Who this book is 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.

Contents:

Cover

Title Page

Copyright and Credits

Dedication

Packt Upsell

Foreword

Contributors

Table of Contents

Preface

Chapter 1: Introduction to Healthcare Analytics

What is healthcare analytics?

Healthcare analytics uses advanced computing technology

Healthcare analytics acts on the healthcare industry (DUH!)

Healthcare analytics improves medical care

Better outcomes

Lower costs

Ensure quality

Foundations of healthcare analytics

Healthcare

Mathematics

Computer science

History of healthcare analytics

Examples of healthcare analytics

Using visualizations to elucidate patient care

Predicting future diagnostic and treatment events

Measuring provider quality and performance

Patient-facing treatments for disease

Exploring the software

Anaconda

Anaconda navigator

Jupyter notebook

Spyder IDE

SQLite

Command-line tools

Installing a text editor

Summary

References

Chapter 2: Healthcare Foundations

Healthcare delivery in the US

Healthcare industry basics

Healthcare financing

Fee-for-service reimbursement

Value-based care

Healthcare policy

Protecting patient privacy and patient rights

Advancing the adoption of electronic medical records

Promoting value-based care

Advancing analytics in healthcare

Patient data - the journey from patient to computer

The history and physical (H&amp

P)

Metadata and chief complaint

History of the present illness (HPI)

Past medical history

Medications

Family history

Social history

Allergies

Review of systems

Physical examination

Additional objective data (lab tests, imaging, and other diagnostic tests)

Assessment and plan

The progress (SOAP) clinical note

Standardized clinical codesets

International Classification of Disease (ICD).

Current Procedural Terminology (CPT)

Logical Observation Identifiers Names and Codes (LOINC)

National Drug Code (NDC)

Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT)

Breaking down healthcare analytics

Population

Medical task

Screening

Diagnosis

Outcome/Prognosis

Response to treatment

Data format

Structured

Unstructured

Imaging

Other data format

Disease

Acute versus chronic diseases

Cancer

Other diseases

Putting it all together - specifying a use case

References and further reading

Chapter 3: Machine Learning Foundations

Model frameworks for medical decision making

Tree-like reasoning

Categorical reasoning with algorithms and trees

Corresponding machine learning algorithms - decision tree and random forest

Probabilistic reasoning and Bayes theorem

Using Bayes theorem for calculating clinical probabilities

Calculating the baseline MI probability

2 x 2 contingency table for chest pain and myocardial infarction

Interpreting the contingency table and calculating sensitivity and specificity

Calculating likelihood ratios for chest pain (+ and -)

Calculating the post-test probability of MI given the presence of chest pain

Corresponding machine learning algorithm - the Naive Bayes Classifier

Criterion tables and the weighted sum approach

Criterion tables

Corresponding machine learning algorithms - linear and logistic regression

Pattern association and neural networks

Complex clinical reasoning

Corresponding machine learning algorithm - neural networks and deep learning

Machine learning pipeline

Loading the data

Cleaning and preprocessing the data

Aggregating data

Parsing data

Converting types

Dealing with missing data

Exploring and visualizing the data

Selecting features.

Training the model parameters

Evaluating model performance

Sensitivity (Sn)

Specificity (Sp)

Positive predictive value (PPV)

Negative predictive value (NPV)

False-positive rate (FPR)

Accuracy (Acc)

Receiver operating characteristic (ROC) curves

Precision-recall curves

Continuously valued target variables

Chapter 4: Computing Foundations - Databases

Introduction to databases

Data engineering with SQL - an example case

Case details - predicting mortality for a cardiology practice

The clinical database

The PATIENT table

The VISIT table

The MEDICATIONS table

The LABS table

The VITALS table

The MORT table

Starting an SQLite session

Data engineering, one table at a time with SQL

Query Set #0 - creating the six tables

Query Set #0a - creating the PATIENT table

Query Set #0b - creating the VISIT table

Query Set #0c - creating the MEDICATIONS table

Query Set #0d - creating the LABS table

Query Set #0e - creating the VITALS table

Query Set #0f - creating the MORT table

Query Set #0g - displaying our tables

Query Set #1 - creating the MORT_FINAL table

Query Set #2 - adding columns to MORT_FINAL

Query Set #2a - adding columns using ALTER TABLE

Query Set #2b - adding columns using JOIN

Query Set #3 - date manipulation - calculating age

Query Set #4 - binning and aggregating diagnoses

Query Set #4a - binning diagnoses for CHF

Query Set #4b - binning diagnoses for other diseases

Query Set #4c - aggregating cardiac diagnoses using SUM

Query Set #4d - aggregating cardiac diagnoses using COUNT

Query Set #5 - counting medications

Query Set #6 - binning abnormal lab results

Query Set #7 - imputing missing variables.

Query Set #7a - imputing missing temperature values using normal-range imputation

Query Set #7b - imputing missing temperature values using mean imputation

Query Set #7c - imputing missing BNP values using a uniform distribution

Query Set #8 - adding the target variable

Query Set #9 - visualizing the MORT_FINAL_2 table

Chapter 5: Computing Foundations - Introduction to Python

Variables and types

Strings

Numeric types

Data structures and containers

Lists

Tuples

Dictionaries

Sets

Programming in Python - an illustrative example

Introduction to pandas

What is a pandas DataFrame?

Importing data

Importing data into pandas from Python data structures

Importing data into pandas from a flat file

Importing data into pandas from a database

Common operations on DataFrames

Adding columns

Adding blank or user-initialized columns

Adding new columns by transforming existing columns

Dropping columns

Applying functions to multiple columns

Combining DataFrames

Converting DataFrame columns to lists

Getting and setting DataFrame values

Getting/setting values using label-based indexing with loc

Getting/setting values using integer-based labeling with iloc

Getting/setting multiple contiguous values using slicing

Fast getting/setting of scalar values using at and iat

Other operations

Filtering rows using Boolean indexing

Sorting rows

SQL-like operations

Getting aggregate row COUNTs

Joining DataFrames

Introduction to scikit-learn

Sample data

Data preprocessing

One-hot encoding of categorical variables

Scaling and centering

Binarization

Imputation

Feature-selection

Machine learning algorithms

Generalized linear models

Ensemble methods

Additional machine learning algorithms.

Performance assessment

Additional analytics libraries

NumPy and SciPy

matplotlib

Chapter 6: Measuring Healthcare Quality

Introduction to healthcare measures

US Medicare value-based programs

The Hospital Value-Based Purchasing (HVBP) program

Domains and measures

The clinical care domain

The patient- and caregiver-centered experience of care domain

Safety domain

Efficiency and cost reduction domain

The Hospital Readmission Reduction (HRR) program

The Hospital-Acquired Conditions (HAC) program

The healthcare-acquired infections domain

The patient safety domain

The End-Stage Renal Disease (ESRD) quality incentive program

The Skilled Nursing Facility Value-Based Program (SNFVBP)

The Home Health Value-Based Program (HHVBP)

The Merit-Based Incentive Payment System (MIPS)

Quality

Advancing care information

Improvement activities

Cost

Other value-based programs

The Healthcare Effectiveness Data and Information Set (HEDIS)

State measures

Comparing dialysis facilities using Python

Downloading the data

Importing the data into your Jupyter Notebook session

Exploring the data rows and columns

Exploring the data geographically

Displaying dialysis centers based on total performance

Alternative analyses of dialysis centers

Comparing hospitals

Exploring the tables

Merging the HVBP tables

Chapter 7: Making Predictive Models in Healthcare

Introduction to predictive analytics in healthcare

Our modeling task - predicting discharge statuses for ED patients

Obtaining the dataset

The NHAMCS dataset at a glance

Downloading the NHAMCS data

Downloading the ED2013 file

Downloading the list of survey items - body_namcsopd.pdf.

Downloading the documentation file - doc13_ed.pdf.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781787283220

1787283224

OCLC:

1048791966