Index

A

• accountable care organizations (ACOs) / Value-based care
• acute-on-chronic diseases / Acute versus chronic diseases
• acute diseases / Acute versus chronic diseases
• AdaBoost / Ensemble methods
• advanced computing technology
  • used, for Healthcare analytics / Healthcare analytics uses advanced computing technology
• Affordable Care Act (ACA) / Promoting value-based care
• All of Us initiative / Advancing analytics in healthcare
• Alternative Payment Models (APM) / Promoting value-based care
• ambulatory care / Healthcare industry basics
• American Medical Association (AMA) / Current Procedural Terminology (CPT)
• Anaconda
  • exploring / Anaconda
• Anaconda navigator / Anaconda navigator
• analytics libraries
  • about / Additional analytics libraries
  • NumPy, reference / NumPy and SciPy
  • SciPy, reference / NumPy and SciPy
  • matplotlib, reference / matplotlib
• Apache Spark
  • reference / What is a pandas DataFrame?
• application programming interface (API) / Healthcare analytics and the internet
• area under the curve (AUC) / Performance assessment

B

• backpropagation technique / Corresponding machine learning algorithm – neural networks and deep learning
• Bayes theorem
  • used, for calculating clinical probabilities / Using Bayes theorem for calculating clinical probabilities
  • baseline MI probability, calculating / Calculating the baseline MI probability
  • for chest pain and myocardial infarction / 2 x 2 contingency table for chest pain and myocardial infarction
  • sensitivity and specificity, calculating / Interpreting the contingency table and calculating sensitivity and specificity
  • contingency table, interpreting / Interpreting the contingency table and calculating sensitivity and specificity
  • likelihood ratios for chest pain (+ and -), calculating / Calculating likelihood ratios for chest pain (+ and -)
  • post-test probability of MI, calculating / Calculating the post-test probability of MI given the presence of chest pain
• binary variables / Exploring and visualizing the data
• Boolean indexing / Filtering rows using Boolean indexing
• Boolean type / Numeric types
• box-and-whisker plot / Exploring and visualizing the data
• bradycardia / Pulse
• Brain Initiative / Advancing analytics in healthcare
• breast cancer prediction example
  • about / An example – breast cancer prediction
  • traditional screening / Traditional screening of breast cancer
  • breast cancer, screening / Breast cancer screening and machine learning
  • machine learning / Breast cancer screening and machine learning
• bundled payments / Value-based care

C

• cancer
  • about / Cancer, Cancer, What is cancer?
  • machine learning applications / ML applications for cancer
  • features / Important features of cancer
  • routine clinical data / Routine clinical data
  • cancer-specific clinical data / Cancer-specific clinical data
  • imaging data / Imaging data
  • genomic data / Genomic data
  • proteomic data / Proteomic data
  • breast cancer prediction example / An example – breast cancer prediction
• Cancer Breakthroughs 2020 / Advancing analytics in healthcare
• cardiovascular disease (CVD) / Overall cardiovascular risk
• cardiovascular risk
  • and machine learning / Cardiovascular risk and machine learning
• categorical data / One-hot encoding of categorical variables
• categorical variables / Exploring and visualizing the data
• Center for Disease Control and Prevention (CDC) / The NHAMCS dataset at a glance
• Centers for Medicare and Medicaid Services (CMS) / Introduction to healthcare measures
• cerebrovascular disease / Overall cardiovascular risk
• chronic diseases / Acute versus chronic diseases
• claudication / Overall cardiovascular risk
• clinical database
  • PATIENT table / The PATIENT table
  • VISIT table / The VISIT table
  • MEDICATION table / The MEDICATIONS table
  • LABS table / The LABS table
  • VITALS table / The VITALS table
  • MORT table / The MORT table
• coefficients / Corresponding machine learning algorithms – linear and logistic regression
• comma-separated values (csv) files / Introduction to pandas
• command-line tools
  • exploring / Command-line tools
• complex clinical reasoning / Complex clinical reasoning
• congestive heart failure (CHF)
  • about / Acute versus chronic diseases, Case details – predicting mortality for a cardiology practice, Overall cardiovascular risk, Congestive heart failure
  • diagnosing / Diagnosing CHF
  • detecting, with machine learning / CHF detection with machine learning
  • applications of machine learning / Other applications of machine learning in CHF
• continuous variables / Exploring and visualizing the data
• convolutional neural networks (CNN) / Healthcare and deep learning
• coronary artery disease (CAD) / Overall cardiovascular risk
• cost function / Training the model parameters
• Current Procedural Terminology (CPT) / Standardized clinical codesets, Current Procedural Terminology (CPT)

D

• data
  • splitting, into train set / Splitting the data into train and test sets
  • splitting, into test set / Splitting the data into train and test sets
• database management system / Introduction to databases
• data engineering, with SQL / Data engineering with SQL – an example case
• data engineering, with SQLite
  • performing / Data engineering, one table at a time with SQL
  • six tables, creating / Query Set #0 – creating the six tables
  • PATIENT table, creating / Query Set #0a – creating the PATIENT table
  • VISIT table, creating / Query Set #0b – creating the VISIT table
  • MEDICATIONS table, creating / Query Set #0c – creating the MEDICATIONS table
  • LABS table / Query Set #0d – creating the LABS table
  • VITALS table, creating / Query Set #0e – creating the VITALS table
  • MORT table, creating / Query Set #0f – creating the MORT table
  • tables, displaying / Query Set #0g – displaying our tables
  • MORT_FINAL table, creating / Query Set #1 – creating the MORT_FINAL table
  • columns, adding to MORT_FINAL / Query Set #2 – adding columns to MORT_FINAL
  • data manipulation / Query Set #3 – date manipulation – calculating age
  • diagnoses, binning / Query Set #4 – binning and aggregating diagnoses
  • diagnoses, aggregating / Query Set #4 – binning and aggregating diagnoses
  • diagnoses, binning for CHF / Query Set #4a – binning diagnoses for CHF
  • diagnoses, binning for other diseases / Query Set #4b – binning diagnoses for other diseases
  • cardiac diagnoses, aggregating with SUM / Query Set #4c – aggregating cardiac diagnoses using SUM
  • cardiac diagnoses, aggregating with COUNT / Query Set #4d – aggregating cardiac diagnoses using COUNT
  • medications, counting / Query Set #5 – counting medications
  • abnormal lab results, binning / Query Set #6 – binning abnormal lab results
  • missing variables, imputing / Query Set #7 – imputing missing variables
  • target variable, adding / Query Set #8 – adding the target variable
  • MORT_FINAL_2 table, visualizing / Query Set #9 – visualizing the MORT_FINAL_2 table
• data format, healthcare analytics
  • structured / Structured
  • unstructured / Unstructured
  • imaging / Imaging
• DataFrame
  • about / Introduction to pandas
  • operations / Common operations on DataFrames, Other operations
  • columns, adding / Adding columns
  • blank or user-initialized columns, adding / Adding blank or user-initialized columns
  • new columns, adding by existing column transformation / Adding new columns by transforming existing columns
  • column, dropping / Dropping columns
  • functions, applying to multiple columns / Applying functions to multiple columns
  • combining / Combining DataFrames
  • columns, converting to lists / Converting DataFrame columns to lists
  • setting / Getting and setting DataFrame values
  • rows, filtering with Boolean indexing / Filtering rows using Boolean indexing
  • rows, sorting / Sorting rows
  • SQL-like operations / SQL-like operations
• DataFrame values
  • getting/setting, with label-based indexing with loc / Getting/setting values using label-based indexing with loc
  • getting/setting, with integer-based labelling with iloc / Getting/setting values using integer-based labeling with iloc
  • multiple contiguous values, getting/setting with slicing / Getting/setting multiple contiguous values using slicing
  • scalar values, getting/setting using at and iat / Fast getting/setting of scalar values using at and iat
• data preprocessing
  • one-hot encoding of categorical variables / One-hot encoding of categorical variables
  • scaling and centering / Scaling and centering
  • binarization / Binarization
  • imputation / Imputation
• dataset
  • importing / Importing the dataset
  • metadata, loading / Loading the metadata
  • ED dataset, loading / Loading the ED dataset
• data structures
  • and containers / Data structures and containers
  • lists / Lists
  • tuples / Tuples
  • dictionaries / Dictionaries
  • sets / Sets
• deep learning
  • about / Corresponding machine learning algorithm – neural networks and deep learning, What is deep learning, briefly?
  • using, in healthcare / Deep learning in healthcare
• deep learning, healthcare
  • deep feed-forward networks / Deep feed-forward networks
  • convolutional neural networks for images / Convolutional neural networks for images
  • recurrent neural networks for sequences / Recurrent neural networks for sequences
• demographic variables, predictor variables
  • age / Age
  • sex / Sex
  • ethnicity / Ethnicity and race
  • demographic information / Other demographic information
• denominator / Introduction to healthcare measures
• diagnosis / Diagnosis
• dialysis facilities, comparing with Python
  • about / Comparing dialysis facilities using Python
  • data, downloading / Downloading the data
  • data, importing into Jupyter Notebook session / Importing the data into your Jupyter Notebook session
  • data rows, exploring / Exploring the data rows and columns
  • data columns, exploring / Exploring the data rows and columns
  • data, exploring geographically / Exploring the data geographically
  • dialysis centers, based on total performance / Displaying dialysis centers based on total performance
  • alternative analyses / Alternative analyses of dialysis centers
• diastolic blood pressure / Blood pressure
• diseases
  • about / Disease
  • acute diseases / Acute versus chronic diseases
  • chronic diseases / Acute versus chronic diseases
  • acute-on-chronic diseases / Acute versus chronic diseases
• dual overlapping frequency histogram / Exploring and visualizing the data

E

• ejection fraction (EF) / Diagnosing CHF
• electrocardiographic (EKG) signals / Other data format
• electroencephalographic (EEG) signals / Other data format
• electronic health record (EHR) / Patient data – the journey from patient to computer
• electronic patient health information (e-PHI) / Protecting patient privacy and patient rights
• electrophysiological signal collection / Other data format
• emergency department (ED) / Using visualizations to elucidate patient care
• emergency medical technicians (EMTs) / Categorical reasoning with algorithms and trees
• End-Stage Renal Disease (ESRD) quality incentive program / The End-Stage Renal Disease (ESRD) quality incentive program
• ensemble methods / Ensemble methods
• epidemic / Healthcare analytics and social media
• error function / Training the model parameters
• ethical issues / Obstacles, ethical issues, and limitations, Ethical issues
• exclusion criteria / Introduction to healthcare measures

F

• feature engineering / Month
• fee-for-service (FFS) payment system / Fee-for-service reimbursement
• fee-for-service (FFS) reimbursement model / US Medicare value-based programs
• Final Omnibus Rule / Protecting patient privacy and patient rights
• final preprocessing steps
  • about / Final preprocessing steps
  • one-hot encoding / One-hot encoding
  • numeric conversion / Numeric conversion
  • NumPy array conversion / NumPy array conversion
• fixed-width format (fwf) files / Introduction to pandas
• flat file / Introduction to pandas
• Food and Drug Administration (FDA) / Advancing analytics in healthcare
• forget gates / Recurrent neural networks for sequences
• forward and backward step-wise regression / Selecting features
• foundations, healthcare analytics
  • healthcare / Healthcare
  • mathematics / Mathematics
  • computer science / Computer science
• Framingham Risk Score / The Framingham Risk Score

G

• greedy approach / Corresponding machine learning algorithms – decision tree and random forest

H

• healthcare
  • and Internet of Things / Healthcare and the Internet of Things
  • and deep learning / Healthcare and deep learning
• healthcare analytics
  • about / What is healthcare analytics?, Healthcare analytics acts on the healthcare industry (DUH!)
  • advanced computing technology, using / Healthcare analytics uses advanced computing technology
  • benefits / Healthcare analytics improves medical care, Lower costs
  • foundations / Foundations of healthcare analytics
  • history / History of healthcare analytics
  • examples / Examples of healthcare analytics
  • visualizations, using to elucidate patient care / Using visualizations to elucidate patient care
  • future diagnostic, predicting / Predicting future diagnostic and treatment events
  • treatment events, predicting / Predicting future diagnostic and treatment events
  • provider quality, measuring / Measuring provider quality and performance
  • provider performance, measuring / Measuring provider quality and performance
  • neuroprosthetics / Patient-facing treatments for disease
  • breaking down / Breaking down healthcare analytics
  • population / Population
  • medical task / Medical task
  • data format / Data format
  • disease / Disease
  • and internet / Healthcare analytics and the internet
  • social media / Healthcare analytics and social media
  • influenza surveillance and forecasting / Influenza surveillance and forecasting
  • suicidality with machine learning, predicting / Predicting suicidality with machine learning
• healthcare delivery
  • in US / Healthcare delivery in the US
• Healthcare Effectiveness Data and Information Set (HEDIS) / The Healthcare Effectiveness Data and Information Set (HEDIS)
• healthcare financing
  • about / Healthcare financing
  • fee-for-service reimbursement / Fee-for-service reimbursement
  • value-based care / Value-based care
• healthcare industry
  • basics / Healthcare industry basics
• healthcare measures / Introduction to healthcare measures
• healthcare policy
  • about / Healthcare policy
  • patient privacy, protecting / Protecting patient privacy and patient rights
  • patient rights, protecting / Protecting patient privacy and patient rights
  • adoption of electronic medical records, advancing / Advancing the adoption of electronic medical records
  • value-based care, prompting / Promoting value-based care
  • analytics, advancing / Advancing analytics in healthcare
• Health Information Technology for Economic and Clinical Health (HITECH) Act / Advancing the adoption of electronic medical records
• Health Insurance Portability and Accountability Act (HIPAA) / Protecting patient privacy and patient rights
• Home Health Agencies (HHAs) / The Home Health Value-Based Program (HHVBP)
• Home Health Value-Based Program (HHVBP) / The Home Health Value-Based Program (HHVBP)
• Hospital-Acquired Conditions (HAC) program
  • about / The Hospital-Acquired Conditions (HAC) program
  • healthcare-acquired infections domain / The healthcare-acquired infections domain
  • patient safety domain / The patient safety domain
• Hospital Compare dataset
  • downloading / Downloading the data
  • data, importing into Jupyter Notebook session / Importing the data into your Jupyter Notebook session
  • tables, exploring / Exploring the tables
  • HVBP tables, merging / Merging the HVBP tables
• Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) / The patient- and caregiver-centered experience of care domain
• Hospital Readmission Reduction (HRR) program / The Hospital Readmission Reduction (HRR) program
• hospitals
  • comparing / Comparing hospitals
• Hospital Value-Based Purchasing (HVBP) program
  • about / The Hospital Value-Based Purchasing (HVBP) program
  • domains / Domains and measures
  • measures / Domains and measures
  • clinical care domain / The clinical care domain
  • patient- and caregiver-centered experience of care domain / The patient- and caregiver-centered experience of care domain
  • safety domain / Safety domain
  • efficiency and cost reduction domain / Efficiency and cost reduction domain
• hypertension / Blood pressure
• hyperthermia / Temperature
• hypotension / Blood pressure
• hypothermia / Temperature

I

• inclusion criteria / Introduction to healthcare measures
• inpatient care / Healthcare industry basics
• input gates / Recurrent neural networks for sequences
• integer-based indexing / Getting and setting DataFrame values
• International Classification of Disease (ICD) / Standardized clinical codesets, International Classification of Disease (ICD)

J

• Jupyter Notebook / Anaconda, Jupyter notebook
• Jupyter session
  • starting / Starting a Jupyter session

K

• keys / Dictionaries

L

• label-based indexing
  • values, getting/setting / Getting and setting DataFrame values
• life expectancy (LE) / Healthcare delivery in the US
• likelihood ratio / Calculating likelihood ratios for chest pain (+ and -)
• limitations / Obstacles, ethical issues, and limitations, Limitations
• linear regression / Corresponding machine learning algorithms – linear and logistic regression
• Logical Observation Identifiers Names and Codes (LOINC) / Standardized clinical codesets, Logical Observation Identifiers Names and Codes (LOINC)
• logistic regression
  • about / Corresponding machine learning algorithms – linear and logistic regression
  • model, building / Logistic regression
• logistic regression model
  • building / Logistic regression
• logit transformation / Generalized linear models
• long short-term memory networks (LSTMs) / Recurrent neural networks for sequences

M

• machine learning
  • CHF detection / CHF detection with machine learning
• machine learning algorithms
  • about / Machine learning algorithms
  • generalized linear models / Generalized linear models
  • ensemble methods / Ensemble methods
  • additional machine learning algorithms / Additional machine learning algorithms
• machine learning pipeline
  • about / Machine learning pipeline
  • data, loading / Loading the data
  • data, cleaning / Cleaning and preprocessing the data
  • data, preprocessing / Cleaning and preprocessing the data
  • data, aggregating / Aggregating data
  • data, parsing / Parsing data
  • converting types / Converting types
  • missing data, dealing with / Dealing with missing data
  • data, exploring / Exploring and visualizing the data
  • data, visualizing / Exploring and visualizing the data
  • features, selecting / Selecting features
  • model parameters, training / Training the model parameters
  • model performance, evaluating / Evaluating model performance
• maxpooling / Convolutional neural networks for images
• mean imputation / Imputation, Wait time
• Medicaid / Healthcare financing
• medical decision making
  • model framework / Model frameworks for medical decision making
• medical task, healthcare analytics
  • screening / Screening
  • diagnosis / Diagnosis
  • outcome / Outcome/Prognosis
  • prognosis / Outcome/Prognosis
  • response to treatment / Response to treatment
• Medicare / Healthcare financing
• Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) / Promoting value-based care
• Merit-Based Incentive Payment System (MIPS)
  • about / Promoting value-based care, The Merit-Based Incentive Payment System (MIPS)
  • quality / Quality
  • advancing care information / Advancing care information
  • improvement activities / Improvement activities
  • cost / Cost
• MetaMap
  • reference / Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT)
• missing variables
  • imputing / Query Set #7 – imputing missing variables
  • temperature values, imputing with normal-range imputation / Query Set #7a – imputing missing temperature values using normal-range imputation
  • temperature values, imputing with imputation / Query Set #7b – imputing missing temperature values using mean imputation
  • BNP values, imputing with uniform distribution / Query Set #7c – imputing missing BNP values using a uniform distribution
• ML applications
  • for cancer / ML applications for cancer
• model frameworks, medical decision making
  • tree-like reasoning / Tree-like reasoning
  • probabilistic reasoning / Probabilistic reasoning and Bayes theorem
  • Bayes theorem / Probabilistic reasoning and Bayes theorem
  • criterion tables / Criterion tables and the weighted sum approach, Criterion tables
  • weighted sum approach / Criterion tables and the weighted sum approach
  • pattern association / Pattern association and neural networks
  • neural networks / Pattern association and neural networks
• model performance evaluation
  • about / Evaluating model performance
  • sensitivity (Sn) / Sensitivity (Sn)
  • specificity (Sp) / Specificity (Sp)
  • positive predictive value (PPV) / Positive predictive value (PPV)
  • negative predictive value (NPV) / Negative predictive value (NPV)
  • false-positive rate (FPR) / False-positive rate (FPR)
  • accuracy (Acc) / Accuracy (Acc)
  • receiver operating characteristic (ROC) curves / Receiver operating characteristic (ROC) curves
  • precision-recall curves / Precision-recall curves
  • continuously valued target variables / Continuously valued target variables
• models
  • used, for making predictions / Using the models to make predictions
  • improving / Improving our models
• modules / Programming in Python – an illustrative example
• mortality, for cardiology practice
  • predicting / Case details – predicting mortality for a cardiology practice
  • clinical database / The clinical database
• mortality rate / The clinical care domain
• MORT_FINAL
  • columns, adding / Query Set #2 – adding columns to MORT_FINAL
  • columns, adding with ALTER TABLE / Query Set #2a – adding columns using ALTER TABLE
  • columns, adding with JOIN / Query Set #2b – adding columns using JOIN
• multiclass problems / Corresponding machine learning algorithm – neural networks and deep learning
• myocardial infarction (MI) / Calculating the baseline MI probability

N

• Naive Bayes Classifier / Corresponding machine learning algorithm – the Naive Bayes Classifier
• National Committee for Quality Assurance (NCQA) / The Healthcare Effectiveness Data and Information Set (HEDIS)
• National Drug Code (NDC) / Standardized clinical codesets, National Drug Code (NDC)
• National Hospital Ambulatory Medical Care Survey (NHAMCS) / The NHAMCS dataset at a glance
• National Institutes of Health (NIH) / Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT)
• neural network model / Neural network
• neural networks / Corresponding machine learning algorithm – neural networks and deep learning
• neuroprosthetics / Patient-facing treatments for disease
• NHAMCS data
  • downloading / Downloading the NHAMCS data
  • ED2013 file, downloading / Downloading the ED2013 file
  • list of survey items, downloading / Downloading the list of survey items – body_namcsopd.pdf
  • documentation file, downloading / Downloading the documentation file – doc13_ed.pdf
• NHAMCS dataset / The NHAMCS dataset at a glance
• numerator / Introduction to healthcare measures

O

• 1-of-K encoding scheme / One-hot encoding of categorical variables
• obstacles / Obstacles, ethical issues, and limitations, Obstacles
• one-hot encoding / One-hot encoding of categorical variables, One-hot encoding
• outpatient / Healthcare industry basics
• output gates / Recurrent neural networks for sequences
• over-the-counter (OTC) medications / Medications

P

• pandas
  • about / Introduction to pandas
  • reference / Introduction to pandas, SQL-like operations
  • data, importing / Importing data
  • data, importing from Python data structures / Importing data into pandas from Python data structures
  • data, importing from flat file / Importing data into pandas from a flat file
  • data, importing from database / Importing data into pandas from a database
• pandas DataFrame / What is a pandas DataFrame?
• pathology / Convolutional neural networks for images
• patient-centered medical homes (PCMHs) / Value-based care
• patient data / The history and physical (H
  • about / Patient data – the journey from patient to computer
  • progress (SOAP) clinical note / The progress (SOAP) clinical note
• patient history / Patient data – the journey from patient to computer
• Patient Protection and Affordable Care Act (PPACA) / Promoting value-based care
• Patient Safety Indicator (PSI) / The Hospital-Acquired Conditions (HAC) program
• peripheral vascular disease (PVD) / Overall cardiovascular risk
• Physician Quality Reporting System (PQRS) / The Merit-Based Incentive Payment System (MIPS)
• Precision Medicine Initiative / Advancing analytics in healthcare
• predictions
  • making, models used / Using the models to make predictions
• predictive healthcare analytics
  • about / Introduction to predictive analytics in healthcare, Predictive healthcare analytics – state of the art
  • modeling tasks / Our modeling task – predicting discharge statuses for ED patients
• predictor variables
  • preprocessing / Preprocessing the predictor variables
  • visit information / Visit information
  • demographic variables / Demographic variables
  • triage variables / Triage variables
  • financial variables / Financial variables
  • vital signs / Vital signs
  • reason-for-visit variables / Reason-for-visit codes
  • injury codes / Injury codes
  • diagnostic codes / Diagnostic codes
  • medical history / Medical history
  • tests / Tests
  • procedures / Procedures
  • medication codes / Medication codes
  • provider information / Provider information
  • disposition information / Disposition information
  • imputed columns / Imputed columns
  • variables identification / Identifying variables
  • electronic medical record status columns / Electronic medical record status columns
  • detailed medication information / Detailed medication information
  • miscellaneous information / Miscellaneous information
• Primary care practitioners (PCPs) / Healthcare industry basics
• prognosis / Outcome/Prognosis
• protected health information (PHI) / Protecting patient privacy and patient rights
• pulmonary embolism (PE) / Criterion tables
• Python
  • programming / Programming in Python – an illustrative example

Q

• Quality Payment Program (QPP) / The Merit-Based Incentive Payment System (MIPS)

R

• random forest / Corresponding machine learning algorithms – decision tree and random forest, Random forest
• readmission modeling / Readmission modeling
• readmission prediction
  • about / Readmission prediction
  • HOSPITAL scores / LACE and HOSPITAL scores
  • LACE score / LACE and HOSPITAL scores
• recurrent neural networks (RNN) / Healthcare and deep learning
• response variable
  • making / Making the response variable

S

• scatterplot / Exploring and visualizing the data
• scikit-learn
  • reference / Introduction to scikit-learn, Scaling and centering
  • sample data / Sample data
  • data preprocessing / Data preprocessing
  • feature-selection / Feature-selection
  • machine learning algorithms / Machine learning algorithms
  • performance assessment / Performance assessment
• screening / Screening
• secondary care / Healthcare industry basics
• Skilled Nursing Facility Value-Based Program (SNFVBP) / The Skilled Nursing Facility Value-Based Program (SNFVBP)
  • reference / The Skilled Nursing Facility Value-Based Program (SNFVBP)
• software
  • exploring / Exploring the software
• software engineering / Computer science
• Spyder IDE / Spyder IDE
• SQL
  • data engineering / Data engineering with SQL – an example case
• SQL-like operations
  • performing / SQL-like operations
  • aggregate row COUNTs, obtaining / Getting aggregate row COUNTs
  • DataFrame, joining / Joining DataFrames
• SQLite
  • exploring / SQLite
• SQLite session
  • starting / Starting an SQLite session
• standardized clinical codesets
  • about / Standardized clinical codesets
  • International Classification of Disease (ICD) / International Classification of Disease (ICD)
  • Current Procedural Terminology (CPT) / Current Procedural Terminology (CPT)
  • Logical Observation Identifiers Names and Codes (LOINC) / Logical Observation Identifiers Names and Codes (LOINC)
  • National Drug Code (NDC) / National Drug Code (NDC)
  • Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) / Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT)
• structured data / Structured, Machine learning pipeline
• Structured Query Language (SQL) / Computer science, Loading the data
• subjective, objective, assessment, and plan (SOAP) / The progress (SOAP) clinical note
• Systematized Nomenclature of Medicine (SNOMED) / Standardized clinical codesets
• Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) / Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT)
• systolic blood pressure / Blood pressure

T

• tachycardia / Pulse
• temperature / Temperature
• tertiary care / Healthcare industry basics
• testing set / Splitting the data into train and test sets
• text editor
  • installing / Installing a text editor
• training set / Splitting the data into train and test sets
• tree-like reasoning
  • about / Tree-like reasoning
  • categorical reasoning with algorithms and trees / Categorical reasoning with algorithms and trees
  • corresponding machine learning algorithms / Corresponding machine learning algorithms – decision tree and random forest

U

• unstructured data / Unstructured
• US Medicare value-based programs
  • about / US Medicare value-based programs
  • Hospital Value-Based Purchasing (HVBP) program / The Hospital Value-Based Purchasing (HVBP) program
  • Hospital Readmission Reduction (HRR) program / The Hospital Readmission Reduction (HRR) program
  • Hospital-Acquired Conditions (HAC) program / The Hospital-Acquired Conditions (HAC) program
  • End-Stage Renal Disease (ESRD) quality incentive program / The End-Stage Renal Disease (ESRD) quality incentive program
  • Skilled Nursing Facility Value-Based Program (SNFVBP) / The Skilled Nursing Facility Value-Based Program (SNFVBP)
  • Home Health Value-Based Program (HHVBP) / The Home Health Value-Based Program (HHVBP)
  • Merit-Based Incentive Payment System (MIPS) / The Merit-Based Incentive Payment System (MIPS)

V

• value-based care / Value-based care
• value-based programs
  • about / Other value-based programs
  • Healthcare Effectiveness Data and Information Set (HEDIS) / The Healthcare Effectiveness Data and Information Set (HEDIS)
  • state measures / State measures
• Value Modifier (VM) program / The Merit-Based Incentive Payment System (MIPS)
• values / Dictionaries
• variable types
  • about / Converting types, Variables and types
  • strings / Strings
  • numeric types / Numeric types
• visit information, predictor variables
  • month / Month
  • day of week / Day of the week
  • arrival time / Arrival time
  • wait time / Wait time
  • visit information / Other visit information
• vital signs, predictor variables
  • temperature / Temperature
  • pulse / Pulse
  • respiratory rate / Respiratory rate
  • blood pressure / Blood pressure
  • oxygen saturation / Oxygen saturation
  • pain level / Pain level

W

• WANDA / Healthcare and the Internet of Things
• World Health Organization (WHO) / International Classification of Disease (ICD)

Z

• zero imputation / Imputation