Statistical dependence-aware biological predictive system
AssigneeMicrosoft Technology Licensing, LLC
InventorsHayden Sullivan Helm, Guodong Chen, Weiwei Yang, Kateryna Lytvynets
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Abstract
A computer implemented method includes accessing a multivariate time series set of samples collected by multiple biological sensors sensing a first biological function over a first period of time, dividing the data set into windows, calculating statistical dependencies between the samples of the timeseries data collected by each sensor, generating a relationship matrix as a function of the statistical dependencies, and transforming the relationship matrix to generate a first feature vector for each window of time that captures the statistical dependencies amongst the sensors.
Claims16
A computer implemented method comprising:
- accessing a multivariate time series set of samples collected by multiple (N) biological sensors sensing a first biological function over a first period of time;
- dividing the data set into non-overlapping windows of time;
- calculating statistical dependencies for each pair of sensors between the samples of the timeseries data collected by each sensor;
- generating an N×N relationship matrix as a function of the statistical dependencies for each pair of sensors;
- transforming the relationship matrix using a dimensionality reduction technique to generate a first feature vector for each window of time that captures the statistical dependencies amongst the sensors;
- training a machine learning model based on the first feature vectors comprising training data;
- receiving input multivariate time series data collected by multiple biological sensors;
- generating input feature vectors in the same manner as the first feature vectors;
- and
- classifying the input feature vectors via the trained machine learning model.
A machine-readable storage device having instructions for execution by a processor of a machine to cause the processor to perform operations to perform a method, the operations comprising:
- accessing a multivariate time series set of samples collected by multiple (N) biological sensors sensing a first biological function over a first period of time;
- dividing the data set into non-overlapping windows of time;
- calculating statistical dependencies for each pair of sensors between the samples of the timeseries data collected by each sensor;
- generating an N×N relationship matrix as a function of the statistical dependencies for each pair of sensors;
- transforming the relationship matrix using a dimensionality reduction technique to generate a first feature vector for each window of time that captures the statistical dependencies amongst the sensors;
- training a machine learning model based on the first feature vectors comprising training data:
- receiving input multivariate time series data collected by multiple biological sensors;
- generating input feature vectors in the same manner as the first feature vectors;
- and
- classifying the input feature vectors via the trained machine learning model.
A device comprising:
- a processor;
- and
- a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising:
- accessing a multivariate time series set of samples collected by multiple (N) biological sensors sensing a first biological function over a first period of time;
- dividing the data set into non-overlapping windows of time;
- calculating statistical dependencies for each pair of sensors between the samples of the timeseries data collected by each sensor;
- generating an N×N relationship matrix as a function of the statistical dependencies for each pair of sensors;
- transforming the relationship matrix using a dimensionality reduction technique to generate a first feature vector for each window of time that captures the statistical dependencies amongst the sensors;
- training a machine learning model based on the first feature vectors comprising training data;
- receiving input multivariate time series data collected by multiple biological sensors;
- generating input feature vectors in the same manner as the first feature vectors;
- and
- classifying the input feature vectors via the trained machine learning model.
Dependencies are inferred from the claim text.