Analytic prediction of antibiotic susceptibility
AssigneeKoninklijke Philips N.V.
InventorKarthikeyan Murugesan
Life of this patent
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Abstract
Methods and systems for predicting the susceptibility of bacterial pathogens to antibiotics using genomic data sets. Various embodiments described herein receive a genomic dataset and a set of labels and run principal variance component analysis thereon to determine the effect sizes of the labels. One or more labels are then selected based on their effect sizes and used in a machine learning model to make predictions on future datasets.
Claims14
A method of training a model for predicting antibiotic drug susceptibility, the method comprising:
- receiving, via an interface, a data set of a plurality of features;
- receiving, via the interface, a set of labels that are related to the plurality of features, wherein the set of labels is a covariate matrix including a plurality of phenotypes, and the plurality of phenotypes includes susceptibilities to various antibiotics and at least one of age, year, isolate collection date, isolate sequencing date, and variation in a sequence measured by multilocous sequence typing;
- supplying the data set of the plurality of features and the set of labels to a processor configured to execute instructions stored on a memory to provide a variance analysis engine, wherein the variance analysis engine is configured to generate an effect size for each of the labels on the data set of the plurality of features;
- supplying as output from the variance analysis engine to a sizing module at least one effect size generated for a label;
- selecting, via the sizing module, at least one label to be used in a machine learning model based on the at least one supplied effect size;
- and
- training, using the selected at least one label with a data set, a machine learning model to predict antibiotic drug susceptibility.
A system for training a model for predicting antibiotic drug susceptibility, the system comprising:
- an interface for receiving a data set of a plurality of features and a set of labels that are related to the plurality of features;
- wherein the set of labels is a covariate matrix including a plurality of phenotypes, and the plurality of phenotypes includes susceptibilities to various antibiotics and at least one of age, year, isolate collection date, isolate sequencing date, and variation in a sequence measured by multilocous sequence typing;
- a memory;
- and
- a processor configured to execute instructions stored on the memory to:
- (i) provide a variance analysis engine configured to receive the data set of the plurality of features and the set of labels;
- (ii) output an effect size for each of the labels, wherein at least one label is selected to be used in a machine learning model based on its effect size;
- and (iii) train, using the selected at least one label with a data set, a machine learning model to predict antibiotic drug susceptibility.
Dependencies are inferred from the claim text.