Patent ID: 8977506
Filing Date: 2015-03-10
Classification: G06N,G16B

Abstract:
1. A method comprising: receiving data, wherein said data comprises one or more characteristics for each cellular constituent in a plurality of cellular constituents that have been measured in a single biological specimen from a patient; applying a plurality of pre-trained models using said data to produce a model score for each respective pre-trained model in said plurality of pre-trained models thereby obtaining a plurality of model scores, wherein each model score in the plurality of model scores is a number that represents the likelihood of a corresponding biological feature represented by the corresponding model being present in the patient, wherein prior to applying the plurality of pre-trained models it is deemed unknown whether the patient has the corresponding biological feature, wherein applying a respective model in said plurality of models comprises determining the model score for the respective model using one or more characteristics for a sub-plurality of cellular constituents in said plurality of cellular constituents in said data that have been measured in the patient, the plurality of pre-trained models comprising a first pre-trained model using one or more characteristics of a first sub-plurality of cellular constituents in said plurality of cellular constituents and a second pre-trained model using one or more characteristics of a second sub-plurality of cellular constituents in said plurality of cellular constituents, wherein the first sub-plurality of cellular constituents includes at least one cellular constituent that is not present in the second sub-plurality of cellular constituents; communicating said plurality of model scores; and treating the patient for a biological feature associated with a model score representing that there is a high likelihood the biological feature is present in the patient.