Systems and methods for identifying liver vascular anomalies
AssigneeIDEXX Laboratories, Inc.
InventorMary Alexis Seguin
Life of this patent
Estimating the term…
Abstract
A method for identifying liver vascular anomalies includes receiving new patient data, receiving a machine-learning based diagnostic model and a knowledge based diagnostic model, determining whether the machine-learning based diagnostic model indicates a risk for liver vascular anomalies for the new patient data, and in a case where the machine-learning based diagnostic model indicates a risk for liver vascular anomalies for the new patient data, assessing the risk of liver vascular anomalies using the knowledge based diagnostic model.
Claims20
A method for identifying liver vascular anomalies comprising:
- receiving new patient data;
- receiving a machine-learning based diagnostic model and a knowledge based diagnostic model;
- determining whether the machine-learning based diagnostic model indicates a risk for liver vascular anomalies for the new patient data;
- and
- in a case where the machine-learning based diagnostic model indicates a risk for liver vascular anomalies for the new patient data, assessing the risk of liver vascular anomalies using the knowledge based diagnostic model.
A diagnostic system for assessing the risk of liver vascular anomalies, the system comprising:
- a memory configured to store instructions;
- an analyzer configured to analyze a patient sample;
- and
- a processor communicatively connected to the memory and the analyzer and configured to execute the stored instructions to:
- receive new patient data from the analyzer;
- receive a machine-learning based diagnostic model and a knowledge based diagnostic model;
- determine whether the machine-learning based diagnostic model indicates a risk for liver vascular anomalies for the new patient data;
- and
- in a case where the machine-learning based diagnostic model indicates a risk for liver vascular anomalies for the new patient data, assess the risk of liver vascular anomalies using the knowledge based diagnostic model.
Dependencies are inferred from the claim text.