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Healthcare Analytics Made Simple
Inrecentyears,machinelearningtechnologiesandanalyticshavebeenwidelyutilizedacrossthehealthcaresector.HealthcareAnalyticsMadeSimplebridgesthegapbetweenpractisingdoctorsanddatascientists.Itequipsthedatascientists’workwithhealthcaredataandallowsthemtogainbetterinsightfromthisdatainordertoimprovehealthcareoutcomes.Thisbookisacompleteoverviewofmachinelearningforhealthcareanalytics,brieflydescribingthecurrenthealthcarelandscape,machinelearningalgorithms,andPythonandSQLprogramminglanguages.Thestep-by-stepinstructionsteachyouhowtoobtainrealhealthcaredataandperformdescriptive,predictive,andprescriptiveanalyticsusingpopularPythonpackagessuchaspandasandscikit-learn.Thelatestresearchresultsindiseasedetectionandhealthcareimageanalysisarereviewed.Bytheendofthisbook,youwillunderstandhowtousePythonforhealthcaredataanalysis,howtoimport,collect,clean,andrefinedatafromelectronichealthrecord(EHR)surveys,andhowtomakepredictivemodelswiththisdatathroughreal-worldalgorithmsandcodeexamples.
最新章節(jié)
- Leave a review - let other readers know what you think
- Other Books You May Enjoy
- References and further reading
- Conclusion of this book
- Limitations
- Ethical issues
品牌：中圖公司
上架時間：2021-07-23 15:50:19
出版社：Packt Publishing
本書數(shù)字版權(quán)由中圖公司提供，并由其授權(quán)上海閱文信息技術(shù)有限公司制作發(fā)行
- Leave a review - let other readers know what you think 更新時間：2021-07-23 17:19:31
- Other Books You May Enjoy
- References and further reading
- Conclusion of this book
- Limitations
- Ethical issues
- Obstacles
- Obstacles ethical issues and limitations
- Recurrent neural networks for sequences
- Convolutional neural networks for images
- Deep feed-forward networks
- Deep learning in healthcare
- What is deep learning briefly?
- Healthcare and deep learning
- Predicting suicidality with machine learning
- Influenza surveillance and forecasting
- Healthcare analytics and social media
- Healthcare and the Internet of Things
- Healthcare analytics and the internet
- The Future – Healthcare and Emerging Technologies
- References and further reading
- Summary
- Other conditions and events
- Readmission modeling
- LACE and HOSPITAL scores
- Readmission prediction
- Breast cancer screening and machine learning
- Traditional screening of breast cancer
- An example – breast cancer prediction
- Proteomic data
- Genomic data
- Imaging data
- Cancer-specific clinical data
- Routine clinical data
- Important features of cancer
- ML applications for cancer
- What is cancer?
- Cancer
- Other applications of machine learning in CHF
- CHF detection with machine learning
- Diagnosing CHF
- Congestive heart failure
- Cardiovascular risk and machine learning
- The Framingham Risk Score
- Overall cardiovascular risk
- Predictive healthcare analytics – state of the art
- Healthcare Predictive Models – A Review
- References and further reading
- Summary
- Improving our models
- Using the models to make predictions
- Neural network
- Random forest
- Logistic regression
- Building the models
- NumPy array conversion
- Numeric conversion
- One-hot encoding
- Final preprocessing steps
- Miscellaneous information
- Detailed medication information
- Electronic medical record status columns
- Identifying variables
- Imputed columns
- Disposition information
- Provider information
- Medication codes
- Procedures
- Tests
- Medical history
- Diagnostic codes
- Injury codes
- Reason-for-visit codes
- Pain level
- Oxygen saturation
- Blood pressure
- Respiratory rate
- Pulse
- Temperature
- Vital signs
- Financial variables
- Triage variables
- Other demographic information
- Ethnicity and race
- Sex
- Age
- Demographic variables
- Other visit information
- Wait time
- Arrival time
- Day of the week
- Month
- Visit information
- Preprocessing the predictor variables
- Splitting the data into train and test sets
- Making the response variable
- Loading the ED dataset
- Loading the metadata
- Importing the dataset
- Starting a Jupyter session
- Downloading the documentation file – doc13_ed.pdf
- Downloading the list of survey items – body_namcsopd.pdf
- Downloading the ED2013 file
- Downloading the NHAMCS data
- The NHAMCS dataset at a glance
- Obtaining the dataset
- Our modeling task – predicting discharge statuses for ED patients
- Introduction to predictive analytics in healthcare
- Making Predictive Models in Healthcare
- References
- Summary
- Merging the HVBP tables
- Exploring the tables
- Importing the data into your Jupyter Notebook session
- Downloading the data
- Comparing hospitals
- Alternative analyses of dialysis centers
- Displaying dialysis centers based on total performance
- Exploring the data geographically
- Exploring the data rows and columns
- Importing the data into your Jupyter Notebook session
- Downloading the data
- Comparing dialysis facilities using Python
- State measures
- The Healthcare Effectiveness Data and Information Set (HEDIS)
- Other value-based programs
- Cost
- Improvement activities
- Advancing care information
- Quality
- The Merit-Based Incentive Payment System (MIPS)
- The Home Health Value-Based Program (HHVBP)
- The Skilled Nursing Facility Value-Based Program (SNFVBP)
- The End-Stage Renal Disease (ESRD) quality incentive program
- The patient safety domain
- The healthcare-acquired infections domain
- The Hospital-Acquired Conditions (HAC) program
- The Hospital Readmission Reduction (HRR) program
- Efficiency and cost reduction domain
- Safety domain
- The patient- and caregiver-centered experience of care domain
- The clinical care domain
- Domains and measures
- The Hospital Value-Based Purchasing (HVBP) program
- US Medicare value-based programs
- Introduction to healthcare measures
- Measuring Healthcare Quality
- Summary
- matplotlib
- NumPy and SciPy
- Additional analytics libraries
- Performance assessment
- Additional machine learning algorithms
- Ensemble methods
- Generalized linear models
- Machine learning algorithms
- Feature-selection
- Imputation
- Binarization
- Scaling and centering
- One-hot encoding of categorical variables
- Data preprocessing
- Sample data
- Introduction to scikit-learn
- Joining DataFrames
- Getting aggregate row COUNTs
- SQL-like operations
- Sorting rows
- Filtering rows using Boolean indexing
- Other operations
- Fast getting/setting of scalar values using at and iat
- Getting/setting multiple contiguous values using slicing
- Getting/setting values using integer-based labeling with iloc
- Getting/setting values using label-based indexing with loc
- Getting and setting DataFrame values
- Converting DataFrame columns to lists
- Combining DataFrames
- Applying functions to multiple columns
- Dropping columns
- Adding new columns by transforming existing columns
- Adding blank or user-initialized columns
- Adding columns
- Common operations on DataFrames
- Importing data into pandas from a database
- Importing data into pandas from a flat file
- Importing data into pandas from Python data structures
- Importing data
- What is a pandas DataFrame?
- Introduction to pandas
- Programming in Python – an illustrative example
- Sets
- Dictionaries
- Tuples
- Lists
- Data structures and containers
- Numeric types
- Strings
- Variables and types
- Computing Foundations – Introduction to Python
- References and further reading
- Summary
- Query Set #9 – visualizing the MORT_FINAL_2 table
- Query Set #8 – adding the target variable
- Query Set #7c – imputing missing BNP values using a uniform distribution
- Query Set #7b – imputing missing temperature values using mean imputation
- Query Set #7a – imputing missing temperature values using normal-range imputation
- Query Set #7 – imputing missing variables
- Query Set #6 – binning abnormal lab results
- Query Set #5 – counting medications
- Query Set #4d – aggregating cardiac diagnoses using COUNT
- Query Set #4c – aggregating cardiac diagnoses using SUM
- Query Set #4b – binning diagnoses for other diseases
- Query Set #4a – binning diagnoses for CHF
- Query Set #4 – binning and aggregating diagnoses
- Query Set #3 – date manipulation – calculating age
- Query Set #2b – adding columns using JOIN
- Query Set #2a – adding columns using ALTER TABLE
- Query Set #2 – adding columns to MORT_FINAL
- Query Set #1 – creating the MORT_FINAL table
- Query Set #0g – displaying our tables
- Query Set #0f – creating the MORT table
- Query Set #0e – creating the VITALS table
- Query Set #0d – creating the LABS table
- Query Set #0c – creating the MEDICATIONS table
- Query Set #0b – creating the VISIT table
- Query Set #0a – creating the PATIENT table
- Query Set #0 – creating the six tables
- Data engineering one table at a time with SQL
- Starting an SQLite session
- The MORT table
- The VITALS table
- The LABS table
- The MEDICATIONS table
- The VISIT table
- The PATIENT table
- The clinical database
- Case details – predicting mortality for a cardiology practice
- Data engineering with SQL – an example case
- Introduction to databases
- Computing Foundations – Databases
- References and further reading
- Summary
- Continuously valued target variables
- Precision-recall curves
- Receiver operating characteristic (ROC) curves
- Accuracy (Acc)
- False-positive rate (FPR)
- Negative predictive value (NPV)
- Positive predictive value (PPV)
- Specificity (Sp)
- Sensitivity (Sn)
- Evaluating model performance
- Training the model parameters
- Selecting features
- Exploring and visualizing the data
- Dealing with missing data
- Converting types
- Parsing data
- Aggregating data
- Cleaning and preprocessing the data
- Loading the data
- Machine learning pipeline
- Corresponding machine learning algorithm – neural networks and deep learning
- Complex clinical reasoning
- Pattern association and neural networks
- Corresponding machine learning algorithms – linear and logistic regression
- Criterion tables
- Criterion tables and the weighted sum approach
- Corresponding machine learning algorithm – the Naive Bayes Classifier
- Calculating the post-test probability of MI given the presence of chest pain
- Calculating likelihood ratios for chest pain (+ and -)
- Interpreting the contingency table and calculating sensitivity and specificity
- 2 x 2 contingency table for chest pain and myocardial infarction
- Calculating the baseline MI probability
- Using Bayes theorem for calculating clinical probabilities
- Probabilistic reasoning and Bayes theorem
- Corresponding machine learning algorithms – decision tree and random forest
- Categorical reasoning with algorithms and trees
- Tree-like reasoning
- Model frameworks for medical decision making
- Machine Learning Foundations
- References and further reading
- Summary
- Putting it all together – specifying a use case
- Other diseases
- Cancer
- Acute versus chronic diseases
- Disease
- Other data format
- Imaging
- Unstructured
- Structured
- Data format
- Response to treatment
- Outcome/Prognosis
- Diagnosis
- Screening
- Medical task
- Population
- Breaking down healthcare analytics
- Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT)
- National Drug Code (NDC)
- Logical Observation Identifiers Names and Codes (LOINC)
- Current Procedural Terminology (CPT)
- International Classification of Disease (ICD)
- Standardized clinical codesets
- The progress (SOAP) clinical note
- Assessment and plan
- Additional objective data (lab tests imaging and other diagnostic tests)
- Physical examination
- Review of systems
- Allergies
- Social history
- Family history
- Medications
- Past medical history
- History of the present illness (HPI)
- Metadata and chief complaint
- The history and physical (H&P)
- Patient data – the journey from patient to computer
- Advancing analytics in healthcare
- Promoting value-based care
- Advancing the adoption of electronic medical records
- Protecting patient privacy and patient rights
- Healthcare policy
- Value-based care
- Fee-for-service reimbursement
- Healthcare financing
- Healthcare industry basics
- Healthcare delivery in the US
- Healthcare Foundations
- References
- Summary
- Installing a text editor
- Command-line tools
- SQLite
- Spyder IDE
- Jupyter notebook
- Anaconda navigator
- Anaconda
- Exploring the software
- Patient-facing treatments for disease
- Measuring provider quality and performance
- Predicting future diagnostic and treatment events
- Using visualizations to elucidate patient care
- Examples of healthcare analytics
- History of healthcare analytics
- Computer science
- Mathematics
- Healthcare
- Foundations of healthcare analytics
- Ensure quality
- Lower costs
- Better outcomes
- Healthcare analytics improves medical care
- Healthcare analytics acts on the healthcare industry (DUH!)
- Healthcare analytics uses advanced computing technology
- What is healthcare analytics?
- Introduction to Healthcare Analytics
- Reviews
- Get in touch
- Conventions used
- Download the color images
- Download the example code files
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is searching for authors like you
- About the reviewer
- About the author
- Contributors
- Foreword
- PacktPub.com
- Why subscribe?
- Packt Upsell
- Dedication
- Healthcare Analytics Made Simple
- Copyright and Credits
- Title Page
- 封面
- 封面
- Title Page
- Copyright and Credits
- Healthcare Analytics Made Simple
- Dedication
- Packt Upsell
- Why subscribe?
- PacktPub.com
- 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
- Get in touch
- 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
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-07-23 17:19:31
