초록 ▼

The simultaneous multi access reasoning technology system of the present invention utilizes both existing knowledge and implicit information that can be numerically extracted from training data to provide a method and apparatus for diagnosing disease and treating a patient. This technology further c

The simultaneous multi access reasoning technology system of the present invention utilizes both existing knowledge and implicit information that can be numerically extracted from training data to provide a method and apparatus for diagnosing disease and treating a patient. This technology further comprises a system for receiving patient data from another location, analyzing the data in a trained neural network, producing a diagnostic value, and optionally transmitting the diagnostic value to another location.

대표청구항 ▼

[ We claim:] [1.]1. A computer based system for screening for a disease, prognosing a disease, diagnosing a disease, determining a presence or an absence or a severity of a disease, or a likelihood of obtaining a disease, comprising:a computation station for receiving and processing a patient data f

[ We claim:] [1.]1. A computer based system for screening for a disease, prognosing a disease, diagnosing a disease, determining a presence or an absence or a severity of a disease, or a likelihood of obtaining a disease, comprising:a computation station for receiving and processing a patient data file, said computation station comprising a trained neural network for processing said patient data file and producing an output value corresponding to a presence or an absence or a severity of a disease, or a likelihood of obtaining a disease, wherein said trained neural network is trained with a method comprising:preprocessing patient biomarkers, comprising:selecting patient biomarkers associated with the disease process;statistically and/or computationally testing discriminating power of the selected patient biomarkers individually in linear and/or non-linear combination for indicating the presence or the absence or the severity of the disease, or the likelihood of obtaining the disease;applying statistical, mathematical, or computational tools, and/or expert knowledge for the derivation of secondary inputs to the neural network that are linear or non-linear combinations of the original or transformed biomarkers;selecting only those patient biomarkers or derived secondary inputs that show discriminating power; andtraining the computer-based neural network using the preprocessed patient biomarkers or derived secondary inputs.