Abstract: A clinical decision support system performs a similarity search to determine the probable outcome of applying on a current patient those clinical actions that were performed on similar patients. The system analyzes stored electronic health records of similar patients so as to recommend diagnostic and therapeutic steps for the current patient. The system receives the health record of the patient, determines which clinical actions were already applied on the patient, generates classifiers associated with potential future clinical actions, generates a success value for each health record of another patient using the classifiers, displays the health record of the other patient having the greatest success value, and indicates a proposed clinical action that is to be applied on the patient. The system also calculates a quality value indicating the probability that a sequence of clinical actions that were applied to a similar patient will be successful if applied to the patient.

Abstract: A clinical decision support system performs a similarity search to determine the probable outcome of applying on a current patient those clinical actions that were performed on similar patients. The system analyzes stored electronic health records of similar patients so as to recommend diagnostic and therapeutic steps for the current patient. The system receives the health record of the patient, determines which clinical actions were already applied on the patient, generates classifiers associated with potential future clinical actions, generates a success value for each health record of another patient using the classifiers, displays the health record of the other patient having the greatest success value, and indicates a proposed clinical action that is to be applied on the patient. The system also calculates a quality value indicating the probability that a sequence of clinical actions that were applied to a similar patient will be successful if applied to the patient.

Abstract: A method is described that involves automatically generating a physical behavior curve from a process description; where, the process description describes a process. The method also involves automatically generating a device model for the process from the physical behavior curve; where, the device model is represented in geometric form. The method also involves attempting to automatically generate, with the device model and with a geometric optimization sequence, a circuit design for the process.

Abstract: A method is described that involves automatically generating an Physical behavior curve from a process description; where, the process description describes a process. The method also involves automatically generating a device model for the process from the Physical behavior curve; where, the device model is represented in geometric form. The method also involves attempting to automatically generate, with the device model and with a geometric optimization sequence, a circuit design for the process.