Applying an Instance-specific Model to Longitudinal Clinical Data for Prediction.

Dynamic Bayesian Belief networks (DBNs) have been commonly used to represent temporal data in several domains; however, an ideal representation requires a near perfect mapping between the process being modeled and the DBN. Furthermore, DBNs assume a full set of observations collected at a fixed frequency. Bayesian model selection has arisen to address biased inference and underlying assumptions about the data (e.

Problem-centric organization and visualization of patient imaging and clinical data.

A patient's electronic medical record contains a large amount of unstructured textual information. As patient records become increasingly dense owing to an aging population and increased occurrence of chronic diseases, a tool is needed to help organize and navigate patient data in a way that facilitates a clinician's ability to understand this information and that improves efficiency. A system has been developed for physicians that summarizes clinical information from a patient record.

A tool for improving the longitudinal imaging characterization for neuro-oncology cases.

We describe the development of a prototype tool for the construction of longitudinal cases studies that can be used for teaching files, construction of clinical databases, and for patient education. The test domain is neuro-oncology. The features of the tool include: 1) natural language processing tools to assist structuring report information; 2) integration of imaging data; 3) integration of drug information; 4) target data model that includes the dimensions of space, time, existence, and causality; 5) user interface that provides three levels of information including overview, filtered summarization, and details on demand.

Evaluation of a dynamic bayesian belief network to predict osteoarthritic knee pain using data from the osteoarthritis initiative.

The most common cause of disability in older adults in the United States is osteoarthritis. To address the problem of early disease prediction, we have constructed a Bayesian belief network (BBN) composed of knee OA-related symptoms to support prognostic queries. The purpose of this study is to evaluate a static and dynamic BBN--based on the NIH Osteoarthritis Initiative (OAI) data--in predicting the likelihood of a patient being diagnosed with knee OA.