Healthcare analytics made simple techniques in healthcare computing using machine learning and Python
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 y...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt
2018.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630735006719 |
Tabla de Contenidos:
- 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&
- 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
- 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
- Summary
- References and further reading
- 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
- Summary
- References and further reading
- 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
- Summary
- 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
- Downloading the data
- Importing the data into your Jupyter Notebook session
- Exploring the tables
- Merging the HVBP tables
- Summary
- References
- 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.
