Systems and methods for determining a genomic testing status
Inventors
Shelley, Addison • Padmos, Alexander • Leung, Angel • Wang, Chun-Che • Green, Dominic • Liu, Edward • Donegan, Janet • Sutton, Lauren • He, Lucy • Phadke, Sharang
Assignees
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Publication Number
US-11961592-B2
Publication Date
2024-04-16
Expiration Date
Abstract
A computer-implemented system for identifying a patient for a trial may include at least one processor. The at least one processor may be programmed to receive an indication of a selected trial, the selected trial being associated with a testing status criterion; access a plurality of patient records associated with a patient of a plurality of patients; determine, using a machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; determine that the genomic testing status satisfies the testing status criterion; and include the patient in a subset of the plurality of patients based on the genomic testing status satisfying the testing status criterion.
Core Innovation
The invention provides a system and method for identifying a patient for a trial by determining a genomic testing status for the patient using machine learning. The system receives an indication of a selected trial associated with a testing status criterion and accesses a plurality of patient records associated with a patient of a plurality of patients.
Using a machine learning model and unstructured information from at least one patient record, the system determines a likelihood of an occurrence of genomic testing for the patient. The system determines a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing, and includes the patient in a subset of the plurality of patients based on the genomic testing status satisfying the testing status criterion.
The system displays, on a computing device, a user interface that includes an indicator of the subset and the testing status criterion.
Claims Coverage
The document includes two independent claims (clm-00001 and clm-00020). They cover five inventive features: trial indication and testing status criterion association; machine learning likelihood from unstructured patient-record information; genomic testing status determination from likelihood; criterion satisfaction and subset inclusion; and user interface indicator for the subset and criterion.
Trial indication and testing status criterion association
Receiving an indication of a selected trial, the selected trial being associated with a testing status criterion.
Machine learning likelihood from unstructured patient-record information
Accessing a plurality of patient records associated with a patient of a plurality of patients; determining, using a machine learning model and based on unstructured information from at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient.
Genomic testing status determination from likelihood
Determining a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing.
Criterion satisfaction and subset inclusion
Determining that the genomic testing status satisfies the testing status criterion; including the patient in a subset of the plurality of patients based on the genomic testing status satisfying the testing status criterion.
User interface indicator for subset and criterion
Displaying, on a computing device, a user interface comprising an indicator of the subset of the plurality of patients and the testing status criterion.
Overall, the claim coverage centers on using a machine learning model to predict a likelihood of genomic testing from unstructured patient records, converting that likelihood into a genomic testing status, and selecting patients whose status satisfies a trial-specific testing status criterion, with the selected subset and criterion indicated via a user interface.
Stated Advantages
Not explicitly described in patent.
Documented Applications
Not explicitly described in patent.
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