Radiographic findings from x-rays, CT scans, and MRIs can also be used in models that are related to cancer severity and prognosis. Later in this section, we will look at a breast cancer study that trained a neural network on mammogram features, such as the presence and shape of breast tumor microcalcifications and surrounding skin characteristics (Ayer et al., 2010).
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Foreword
- Contributors
- Preface
- Introduction to Healthcare Analytics
- Healthcare Foundations
- Machine Learning Foundations
- Model frameworks for medical decision making
- Tree-like reasoning
- 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
- Using Bayes theorem for calculating clinical probabilities
- Criterion tables and the weighted sum approach
- Pattern association and neural networks
- Machine learning pipeline
- Summary
- References and further reading
- Model frameworks for medical decision making
- Computing Foundations – Databases
- Introduction to databases
- Data engineering with SQL – an example case
- Case details – predicting mortality for a cardiology practice
- 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 #3 – date manipulation – calculating age
- Query Set #4 – binning and aggregating diagnoses
- Query Set #5 – counting medications
- Query Set #6 – binning abnormal lab results
- Query Set #7 – imputing missing variables
- Query Set #8 – adding the target variable
- Query Set #9 – visualizing the MORT_FINAL_2 table
- Query Set #0 – creating the six tables
- Summary
- References and further reading
- Computing Foundations – Introduction to Python
- Variables and types
- Data structures and containers
- Programming in Python – an illustrative example
- Introduction to pandas
- Introduction to scikit-learn
- Additional analytics libraries
- Summary
- Measuring Healthcare Quality
- Introduction to healthcare measures
- US Medicare value-based programs
- The Hospital Value-Based Purchasing (HVBP) program
- The Hospital Readmission Reduction (HRR) program
- The Hospital-Acquired Conditions (HAC) program
- 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)
- Other value-based programs
- Comparing dialysis facilities using Python
- Comparing hospitals
- 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
- Starting a Jupyter session
- Importing the dataset
- Making the response variable
- Splitting the data into train and test sets
- Preprocessing the predictor variables
- Visit information
- Demographic variables
- Triage variables
- Financial variables
- Vital signs
- 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
- Building the models
- Using the models to make predictions
- Improving our models
- Summary
- References and further reading
- Healthcare Predictive Models – A Review
- The Future – Healthcare and Emerging Technologies
- Other Books You May Enjoy
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