Healthcare Analytics Made Simple by Kumar, Vikas -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Healthcare Analytics Made Simple

Dedication Packt Upsell

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Foreword Contributors

About the author About the reviewer Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Reviews

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

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&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

Summary References and further reading

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

Summary References and further reading

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

Summary References and further reading

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

Summary

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

Downloading the data Importing the data into your Jupyter Notebook session Exploring the tables Merging the HVBP tables

Summary References

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

Starting a Jupyter session Importing the dataset

Loading the metadata Loading the ED dataset

Making the response variable Splitting the data into train and test sets Preprocessing the predictor variables

Visit information

Month Day of the week Arrival time Wait time Other visit information

Demographic variables

Age Sex Ethnicity and race Other demographic information

Triage variables Financial variables Vital signs

Temperature Pulse Respiratory rate Blood pressure Oxygen saturation Pain level

Reason-for-visit codes Injury codes Diagnostic codes Medical history Tests Procedures Medication codes Provider information Disposition information Imputed columns Identifying variables Electronic medical record status columns Detailed medication information Miscellaneous information

Final preprocessing steps

One-hot encoding Numeric conversion NumPy array conversion

Building the models

Logistic regression Random forest Neural network

Using the models to make predictions Improving our models Summary References and further reading

Healthcare Predictive Models – A Review

Predictive healthcare analytics – state of the art Overall cardiovascular risk

The Framingham Risk Score Cardiovascular risk and machine learning

Congestive heart failure

Diagnosing CHF CHF detection with machine learning Other applications of machine learning in CHF

Cancer

What is cancer? ML applications for cancer Important features of cancer

Routine clinical data Cancer-specific clinical data Imaging data Genomic data Proteomic data

An example – breast cancer prediction

Traditional screening of breast cancer Breast cancer screening and machine learning

Readmission prediction

LACE and HOSPITAL scores Readmission modeling

Other conditions and events Summary References and further reading

The Future – Healthcare and Emerging Technologies

Healthcare analytics and the internet

Healthcare and the Internet of Things Healthcare analytics and social media

Influenza surveillance and forecasting Predicting suicidality with machine learning

Healthcare and deep learning

What is deep learning, briefly? Deep learning in healthcare

Deep feed-forward networks Convolutional neural networks for images Recurrent neural networks for sequences

Obstacles, ethical issues, and limitations

Obstacles Ethical issues Limitations

Conclusion of this book References and further reading

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