Healthcare Data Security for Beginners

Explore how data privacy and security affect patients, illustrated by real breaches, including a payer disclosing an HIV case, and the risks of identity theft and impersonation.

Medical record data holds higher value for hackers than credit cards or Social Security numbers, making it easier to commit identity theft.

Compare HIPAA, HITECH, GDPR, and the UK DPA, and learn the core tenets that shape privacy and security of patient data across regions.

Define PHI, or protected health information, including names, addresses, and electronic medical records. Ensure PHI complies with data security and privacy standards, guiding transmission, storage, and model-building use.

Understand how business associates disclose data under privacy law for permitted purposes and apply the business associate agreement addendum (BAA) between a government entity and a business associate.

Encrypt PHI data at rest, use a VPN, and secure data transmission to follow best practices in healthcare data security.

Explore HIPAA de-identification methods for datasets by applying expert determination or Safe Harbor, removing identifiers such as names and addresses to reduce privacy risks while enabling research.

Explore value distributions, including the normal bell-curve distribution, labeled as the gasoline distribution, and the uniform distribution. Also examine skewed, unbalanced, and balanced distributions where a subset dominates.

Explore the evolution of analytics from batch and historical BI to real-time and predictive analytics, using data warehouses and machine learning to forecast behavior and detect threats.

Explore AWS storage options for analytics, including S3 for durable data, Redshift for data warehousing, DynamoDB for NoSQL, and Glacier for infrequent access, integrated with analytic services.

Learn how machine learning, a subfield of artificial intelligence, enables computers to learn automatically from data, improve over time, and use patterns to make informed predictions without ongoing human intervention.

Discover how machine learning drives business value through iterative models that continually optimize predictions, with applications in recommendation engines, demand forecasting, computer vision, fraud detection, and medical diagnosis.

Explore the machine learning lifecycle from dataset collection and feature availability to model training, selecting an algorithm, and testing for accuracy to ensure useful predictions.

Explore supervised learning by training models on a dataset with the correct answers to predict the answers for new data points.

Learn how supervised learning uses training data to create mapping functions, distinguishing classifiers for discrete outputs from regression models for continuous outputs, with fraud detection and temperature forecasting as examples.

Learn to perform supervised training by splitting data into training and testing sets, extracting feature vectors, building a predictive model, evaluating accuracy against labels, and deploying it for new data.

Discover how supervised machine learning answers business problems by selecting training data sources, including home sales, viewing statistics, malignant cancers, and loan default data.

Identify a clustering pattern in travel data, where Mary consistently travels to or from New York on the first of each month at the cost of thirty dollars.

Explore unsupervised training, its key characteristics, how it is used, and example algorithms. The training dataset contains only examples and no specific label or outcome.