Table of Contents CHAPTER 7: ORGANIZING OBSERVATIONS: DATA MODELS 299 Data Models for Representing Medical Data Spatial Data Models.… 300 Spatial Relationships and Reasoning 01 Anatomical and Imaging-based Models.. Temporal Data Models Temporal Relationships and Reasoning Some Open Issues in Temporal Modeling Clinically-oriented Views 16 Alternative Views and Application Domains 318 Discussion and Applications. 319 A Phenomenon-centric View: Supporting Investigation What is a Mass? An Exercise in Separating Observations from Inferences. PCDM Core Entities Implementing the PCDM. PART IV TOWARD MEDICAL DECISION MAKING 333 CHAPTER 8: DISEASE MODELS. PART I: GRAPHICAL MODELS ....................4..335 Uncertainty and Probability .. Why Probabilities? 335 Laws of Probability: A Brief Review 337 Probability and Change Graphical Models 340 Graph Theory…… Graphs and Probabilities Representing Time… Graphs and Causation Bayesian Belief Networks in Medicine Belief Network Construction: Building a Disease Model Causal Infered Causal Models, Interventions and counterfactuals. 351 Latent Projections and their Causal Interpretation. Discussion and Applications 359 Building Belief and Causal Networks: Practical Considerations Accruing Sufficient Patient Data Handling Uncertainty in Data.... andling Selection Bias References 365 CHAPTER 9: DISEASE MODELS, PART II: QUERYING APPLICATIONS 371 Exploring the Network: Queries and Evaluation.. 371 Inference: Answering Queries 371 Belief Updating 372 Abductive Reasoning....Table of Contents xxi CHAPTER 7: ORGANIZING OBSERVATIONS: DATA MODELS ....................................299 Data Models for Representing Medical Data.................................................... 299 Spatial Data Models........................................................................................... 3 Spatial Representations .............................................................................................. 300 Spatial Relationships and Reasoning........................................................................... 301 Anatomical and Imaging-based Models...................................................................... 304 Temporal Data Models ...................................................................................... 308 Representing Time ...................................................................................................... 308 Temporal Relationships and Reasoning ...................................................................... 313 Some Open Issues in Temporal Modeling................................................................... 315 Clinically-oriented Views ................................................................................... 316 Alternative Views and Application Domains ............................................................... 318 Discussion and Applications............................................................................. 319 A Phenomenon-centric View: Supporting Investigation ................................... 319 What is a Mass? An Exercise in Separating Observations from Inferences................. 320 PCDM Core Entities..................................................................................................... 323 Implementing the PCDM............................................................................................. 325 References....................................................................................................... 326 PART IV TOWARD MEDICAL DECISION MAKING.................................................333 CHAPTER 8: DISEASE MODELS, PART I: GRAPHICAL MODELS .................................335 Uncertainty and Probability............................................................................. 335 Why Probabilities?............................................................................................. 335 Laws of Probability: A Brief Review............................................................................. 337 Probability and Change ............................................................................................... 339 Graphical Models............................................................................................... 340 Graph Theory .............................................................................................................. 340 Graphs and Probabilities............................................................................................. 341 Representing Time ...................................................................................................... 343 Graphs and Causation ................................................................................................. 345 Bayesian Belief Networks in Medicine .............................................................. 346 Belief Network Construction: Building a Disease Model............................................. 347 Causal Inference ................................................................................................ 351 Causal Models, Interventions, and Counterfactuals ................................................... 351 Latent Projections and their Causal Interpretation..................................................... 354 Identification............................................................................................................... 355 Discussion and Applications............................................................................. 359 Building Belief and Causal Networks: Practical Considerations ........................ 360 Accruing Sufficient Patient Data ................................................................................. 361 Handling Uncertainty in Data...................................................................................... 363 Handling Selection Bias............................................................................................... 364 References....................................................................................................... 365 CHAPTER 9: DISEASE MODELS, PART II: QUERYING & APPLICATIONS ......................371 Exploring the Network: Queries and Evaluation............................................... 371 Inference: Answering Queries ........................................................................... 371 Belief Updating ........................................................................................................... 372 Abductive Reasoning................................................................................................... 377 00