Patent ID: 11915827
Assignee: KENNETH NEUMANN
Field: Medical technology (Instruments)
Classification: CPC G  Y | IPC G

Claim 0:
1. A system for classification to prognostic labels, the system comprising:
a classification device, the classification device designed and configured to:
receive training data, wherein receiving the training data further comprises:
receiving a first training set including a plurality of first data entries, each first data entry of the plurality of first data entries including at least an element of physiological state data of a user and at least a correlated first prognostic label;
receiving a second training set including a plurality of second data entries, each second data entry of the plurality of second data entries including at least a second prognostic label and at least a correlated ameliorative process label, wherein receiving the second training set comprises receiving:
a medical history document comprising at least a case study associated with the at least a correlated ameliorate process label; and
one or more records of a prognostic label database and an ameliorative process label database as a function of a temporal attribute of the training data set;

record at least a physiological test sample, wherein the at least a physiological test sample comprises a physically extracted sample from the user;
generate at least a prognostic output using a first machine-learning model created by a prognostic label learner operating on the classification device, the prognostic label learner designed and configured to:
determine a respective significance score for each element of physiological state data in the plurality of first data entries such that a plurality of significance scores is determined, wherein determining each significance score further comprises:
receiving a plurality of expert entries from one or more expert users, wherein each entry of the plurality of expert entries identifies at least an element of physiological state data as significant; and
determining the significance score based on the plurality of expert entries;

compare the respective significance score for each element of physiological state data to a predetermined threshold value; and
eliminate one or more elements of physiological state data from the plurality of first data entries based on the comparisons of respective significance scores to the predetermined threshold value; and
generate the at least a prognostic output as a function of the plurality of significance scores for the at least a portion of the plurality of first data entries, the first training set, and the at least a physiological test sample;

generate at least an ameliorative output using a second machine-learning model created by an ameliorative process label learner operating on the classification device, the ameliorative process label learner designed and configured to generate the at least an ameliorative output as a function of the second training set and the at least a prognostic output;
ranking the at least an ameliorative output, wherein ranking the at least an ameliorative output comprises:
generating a best-fit function to the at least an ameliorative output; and
ranking the at least an ameliorative output as a function of respective slopes of the at least an ameliorative output;

generate an updated ameliorative output using longitudinal data associated with an updated medical history document; and
transmit an output including at least a prognostic output and at least an ameliorative output to a user device.