Algorithms to Identify Patients with Hepatocellular Carcinoma
First Claim
1. A computer-implemented method comprising:
- receiving, at a patient identification module via a network interface, patient data describing a plurality of patients;
identifying, by a patient identification module executing on one or more processors, at least some of the plurality of patients as having a high risk of developing liver cancer,wherein the patient identification module is generated based on an application of machine learning techniques to a training data set, andwherein the patient identification module is validated based on both the training data set and an external validation data set; and
generating, by the patient identification module, a grouping of the plurality of patients based on the identification of the at least some of the plurality of patients.
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Abstract
A method for identifying patients with a high risk of liver cancer development includes receiving patient data describing a plurality of patients and executing a patient identification module on the patient data to identify at least some of the plurality of patients as having a high risk of developing liver cancer. The patient identification module is generated based on an application of machine learning techniques to a training data set, and the patient identification module is validated based on both the training data set and an external validation data set. Further, the method includes generating a grouping of the plurality of patients based on the identification of the at least some of the plurality of patients.
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Citations
20 Claims
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1. A computer-implemented method comprising:
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receiving, at a patient identification module via a network interface, patient data describing a plurality of patients; identifying, by a patient identification module executing on one or more processors, at least some of the plurality of patients as having a high risk of developing liver cancer, wherein the patient identification module is generated based on an application of machine learning techniques to a training data set, and wherein the patient identification module is validated based on both the training data set and an external validation data set; and generating, by the patient identification module, a grouping of the plurality of patients based on the identification of the at least some of the plurality of patients. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A computer device specially configured to identify patients with a high risk of liver cancer development, the computer device comprising:
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one or more processors; and one or more non-transitory memories coupled to the one or more processors; wherein the one or more memories include computer executable instructions stored therein that, when executed by the one or more processors, cause the one or more processors to; receive, via a network interface, patient data describing a plurality of patients, execute a patient identification module on the patient data to identify at least some of the plurality of patients as having a high risk of developing liver cancer, wherein the patient identification module is generated based on an application of machine learning techniques to a training data set, and wherein the patient identification module is validated based on both the training data set and an external validation data set, and generate a grouping of the plurality of patients based on the identification of the at least some of the plurality of patients. - View Dependent Claims (17, 18, 19, 20)
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Specification