Abstract: Methods, systems and computer program products for generating a decision to underwrite a life insurance policy for an applicant are provided. Information about the applicant is obtained, an individual mortality ratio value for the applicant is generated using the applicant information, the mortality ratio value is applied to a population mortality value to generate a mortality risk value for the applicant, and an underwriting decision regarding the applicant is generated based on the applicant's mortality risk value. Applicant information includes a plurality of data elements, each data element is associated with a characteristic of the applicant. Generating the individual mortality ratio value for the applicant includes generating a deviation of each data element from a respective mean value, obtaining a mortality relative risk estimate for each respective data element, and generating the individual mortality ratio value (MR) via: MR=exp (ln(RR1)(x1?x1m)+ln(RR2)(x2?x2m) . . . ln(RRn?xnm)).

Abstract: A method and system is provided which takes into account actual human data previously collected. The human data is used to predict the probability of a test conducted on a patient later, yielding a result above a threshold level which would be informative to a doctor treating a patient. The test which is conducted is for detecting levels of C-reactive protein, lipoprotein(a) or homocysteine in a patient. Patient data is collected and input into a multivariate function which then yields a percentage probability that test results on such a patient will be above a predetermined level.

Abstract: A method and apparatus for assessing a person's disease status is provided using a plurality of disease prediction factors for that person and applying a multivariate disease prediction equation to the data to assess the disease status of that person. The multivariate disease prediction equation includes the contribution for each disease prediction factor in a comprehensive set of disease prediction factors. Each of the plurality of disease prediction factors for which data are available from the test person is included in the comprehensive set.