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

Claim 5:
6. A method of classification to prognostic labels, the method comprising:
receiving, at a classification device, 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 and at least a correlated first prognostic label; and
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;

recording, at the classification device, at least a physiological test sample, wherein the at least a physiological test sample comprises a physically extracted sample;
generating, at the classification device, an output as a function of the training data and the at least a physiological test sample, wherein generating the output further comprises:
generating at least a prognostic output using a first machine-learning model created by a prognostic label learner, wherein generating the at least a prognostic output comprises:
determining 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;

comparing the respective significance score for each element of physiological state data to a predetermined threshold value; and
eliminating 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
generating 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; and

generating at least an ameliorative output using a second machine-learning model created by an ameliorative process label learner as a function of the second training set and the at least a prognostic output; and
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;

generating an updated ameliorative output using longitudinal data associated with an updated medical history document; and
transmitting, by the classification device, the output to a user output device.