Kumar - Healthcare analytics made simple: techniques in healthcare computing using machine learning and Python

1. Introduction to healthcare analytics -- What is healthcare analytics? -- Healthcare analytics uses advanced competing technology -- Healthcare analytics acts on the healthcare industry -- Healthcare analytics improves medical care -- Better outcomes -- Lower costs -- Ensure quality -- Foundation of healthcare analytics -- Healthcare -- Mathematics -- Computer science -- History of healthcare analytics -- Examples of healthcare analytic -- 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 -- 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 too computer -- The history and physical -- Metadata and chief complaint -- History of the present illness -- Past medial history -- Medications -- Family history -- Social history -- Allergies -- Review of systems -- Physical examination -- Additional objective data -- Assessment and plan -- The progress clinical note -- Standardized clinical code sets -- International classification of disease -- Current procedural terminology -- Logical observation identifiers names and codes -- National drug code -- Systematized nomenclature of medicine clinical terms -- 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 -- 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 -- 2x2 Contingency table for chest pain and myocardial infarction -- Interpreting the contingency table and calculating sensitivity and specificity -- Calculating likelihood ratios for chest pain -- 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 proprocessing 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 -- Specificity -- Positive predictive value -- Negative predictive value -- False-positive rate -- Accuracy -- receiver operating characteristics curves -- Precision-recall curves -- Continuously valued target variables -- 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 table s-- 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 - data 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 -- 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 panda -- What is a pandas data frame? -- 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 data frames -- Adding columns -- Adding blank or user-initialized columns -- Adding new columns by transforming existing columns -- Dropping columns -- Applying functions to multiple columns -- Combining data frames -- Converting data frame columns to lists -- Getting and setting data frame 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 an iat -- Other operations -- Filtering rows using Boolean indexing -- Sorting rows -- SQL-like operations -- Getting aggregate row COUNTs -- Joining data frames -- 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 -- 6. Measuring healthcare quality -- Introduction to healthcare measures -- US medicare value-based programs -- The hospital value-based purchasing 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 program -- The hospital-acquired conditions program -- The healthcare-acquired infections domain -- The patient safety domain -- The end-stage renal disease quality incentive program -- The skilled nursing facility value-based program -- The home health value-based program -- The merit-based incentive payment system -- Quality -- Advancing care information -- Improvement activities -- Cost -- Other value-based programs -- The healthcare effectiveness data and information set -- State measures -- Comparing dialysis facilities using python -- Downloading the data -- Importing the data into your Jupyter notebook -- 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 -- 7. Making predictive models in healthcare -- Introduction to predictive analytics in healthcare -- Our modeling task - predicting discharge statuses for ED patients -- Obtaining the data set -- The NHAMCA data set at a glance -- Downloading the NHAMCS data -- Downloading the ED2013 -- file -- Downloading the list of survey items - body_namcsoph.pdf -- Downloading the documentation file - doc13_ed.pdf -- Starting a Jupyter session -- Importing the data set -- Loading the meta data -- Loading the ED data set -- Making the response variable -- Splitting the data into train and test sets -- Preprocesing 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 -- NomPy array conversion -- Building the models -- Logistic regression -- Random forests -- Neural network -- Using the models to make predictions -- Improving our models -- 8. 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 -- 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 -- 9. The future - healthcare and emerging technologies -- Healthcare analytics and the internet -- 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;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.