Abstract: An Integrated Virtual Patient Framework (IVPF) for providing decision support that evolves with increasing data collected on a given patient. Support decisions for patients with limited data is made using statistical prediction tools derived from historical trajectories of similar patients. As patient histories grow, decision support is provided by mathematical models that constrain the possible dynamics of the patient to more detailed predictive models. Weights for each model are assigned depending on uncertainties arising from data fitting and model properties. As new data are entered into a patient record, the models are recalibrated and the weights are adjusted, leading to updated decision support information. The framework also suggests the benefit of additional follow-up data collection events, optimizing the data collection as well as how the IVPF generates predictions. The framework also may present scenarios to patients in a way that informs them of treatment outcomes, given various strategies.

Abstract: An Integrated Virtual Patient Framework (IVPF) that incorporates dynamic and mechanistic modeling to provide for testing of finer patient-specific data subdivisions, and also allows non-standard therapies to be queried for success. New measurements of patient follow-up data can be rapidly incorporated into the IVPF in order to dynamically update the optimization of the treatment strategy, making the IVPF a powerful tool for implementing adaptive therapies. The IVPF is built using software is accessible to the nonmathematician. Inputs, options, and decision recommendations are delivered in a fashion that will have clear meaning to the clinician deciding the treatment. The system is adaptable to the different decision processes which are used in the clinic. Each disease has a particular decision set that the framework will be able to handle.