What's included

• Time-Series Analysis in Healthcare Using Probabilistic Graphical Models

  Time-series analysis in healthcare using probabilistic graphical models (PGMs) involves modeling the dependencies between variables in temporal data, such as patient vital signs over time. PGMs like Bayesian Networks and Hidden Markov Models capture uncertainty and temporal dependencies in medical data. These models are useful for predicting patient outcomes, tracking disease progression, and detecting anomalies in real-time monitoring. By handling missing data and incorporating latent factors, PGMs enhance decision-making in healthcare. Applications include predictive modeling, disease forecasting, and personalized treatment plans.