US 10,593,431 B1Granted

Methods and systems for causative chaining of prognostic label classifications

AssigneeKpn Innovations, LLC.

InventorKenneth Neumann

Life of this patent

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Abstract

A system for causative chaining of prognostic label classifications includes a classification device configured to receive training data including a plurality of first data entries, each including at least a first element of physiological state data and at least a correlated first prognostic label and a plurality of second data entries, each including at least a second prognostic label and at least a correlated third prognostic label, and to record at least a first biological extraction. The system includes a prognostic label learner configured to generate at least a first prognostic output as a function of the first training set and the at least a physiological test sample, and a causal link learner configured to generate at least a second prognostic output causally linked to the first prognostic output as a function of the second training set and the at least a first prognostic output.

Claims12

Dependent claims

A system for causative chaining of prognostic label classifications, the system comprising:

at least a computing device, the computing 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 a first 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 third prognostic label;
record at least a first biological extraction;
display one or more follow-up suggestions for acquisition of at least a second biological extraction at a user output device;
and
receive the at least a second biological extraction;
a prognostic label learner operating on the at least a computing device, the prognostic label learner designed and configured to generate at least a first prognostic output as a function of the first training set and the at least a physiological test sample, wherein the prognostic label learner is further configured to generate a third prognostic output as a function of the first training set and the at least a second biological extraction;
and
a causal link learner operating on the at least a computing device, the causal link learner designed and configured to generate at least a second prognostic output as a function of the second training set and the at least a first prognostic output, wherein the at least a second prognostic output represents a cause of the at least a first prognostic output, and wherein the at least a second prognostic output further comprises a plurality of second prognostic outputs;
wherein the at least a computing device is further configured to:
determine that a single prognostic output of the plurality of second prognostic outputs contradicts the third prognostic output;
and
eliminate the single prognostic output from the plurality of second prognostic outputs.

13 more elements

A method of causative chaining of prognostic label classifications, the method comprising:

receiving, by at least a computing 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 a first 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 third prognostic label;
and
recording, by the at least a computing device, at least a first biological extraction;
displaying one or more follow-up suggestions for acquisition of at least a second biological extraction at a user output device;
and
receiving the at least a second biological extraction;
generating, by the computing device, at least a first prognostic output as a function of the first training set and the at least a physiological test sample;
generating, by the at least a computing device, a third prognostic output as a function of the first training set and the at least a second biological extraction;
generating, by the at least a computing device, at least a second prognostic output as a function of the second training set and the at least a first prognostic output, wherein the at least a second prognostic output represents a cause of the at least a first prognostic output, the at least a second prognostic output further comprising a plurality of second prognostic outputs;
determining that a single prognostic output of the plurality of second prognostic outputs contradicts the third prognostic output;
and
eliminating the single prognostic output from the plurality of second prognostic outputs.

12 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/430,387Filed 3 Jun 2019
Granted: 17 Mar 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification24 codes

G16H 50/20
ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or…: for…
G06F 18/2155
Generating training patterns; Bootstrap methods, e.g. bagging or boosting: characterised by the incorporation of unlabelled data, e.g. multiple…
G06N 5/022
Knowledge representation; Symbolic representation: knowledge engineering; Knowledge acquisition
G06N 7/01
Computing arrangements based on specific mathematical models: probabilistic graphical models, e.g. probabilistic networks