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AI Biotech/Diagnostics: General Diagnostics
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Application US20180160894
Published 2018-06-14
Forecasting Eye Condition Progression For Eye Patients
Aspects extend to methods, systems, and computer program products for forecasting eye condition progression for eye patients. When a patient visits an eye practitioner, the patient (or when appropriate their guardian) may be interested in the current eye condition as well as a prediction of eye condition progression in the future and/or as the patient ages. Aspects of the invention can be used to predict the progress of an eye condition for a patient (e.g., a child) at a number of different post-examination times after an examination. Predicting the progress of an eye condition for a patient over time can be used to assist the eye practitioner in tailoring a treatment plan and/or tailoring a subsequent examination schedule for the patient.
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- 1. A computer system, the computer system comprising:
one or more hardware processors; system memory coupled to the one or more hardware processors, the system memory storing instructions that are executable by the one or more hardware processors; the one or more hardware processors executing the instructions stored in the system memory to predict sphere for a patient's eyes, including the following:
access eye characteristic data for the patient's eyes, the eye characteristic data acquired during an eye examination performed on the patient, the eye characteristic data including one or more measured sphere values for the sphere of the patient's eyes;
access demographic data for the patient;
input the eye characteristic data, including the one or more measured sphere values for the sphere of the patient's eyes, and the demographic data into a predictive model in the system memory, the predictive model formulated from other patient data acquired during other eye examinations for a plurality of other patients, the other patient data including, for each other patient included in the plurality of other patients, one or more measured sphere values for the sphere of the other patient's eyes, other eye characteristic data for the other patient's eyes, and demographic data for the other patient, the predictive model transforming the eye characteristic data for the patient, the demographic data for the patient, and the other patient data through linear regression to:
forecast sphere values for the sphere of the patient's eyes at a plurality of different post examination time periods, the forecast sphere values inferred from the eye characteristic data for the patient, including the one or more measured sphere values for the sphere of the patient's eyes, and the demographic data for the patient in view of the other patient data; and
formulate a time series chart of sphere for the patient's eyes from the forecast sphere values, the time series chart forecasting likely sphere values for the sphere of the patient's eyes over a plurality of different time periods in the future; and
return the formulated time series chart as a prediction of changes to the sphere of the patient's eyes as the patient ages.
- 9. A computer system, the computer system comprising:
one or more hardware processors; system memory coupled to the one or more hardware processors, the system memory storing instructions that are executable by the one or more hardware processors; the one or more hardware processors executing the instructions stored in the system memory to predict sphere for a patient's eyes, including the following:
access eye characteristic data for the patient's eyes, the eye characteristic data acquired during a plurality of different eye examinations performed on the patient, each eye examination included in the plurality of different eye examinations performed at a different time and separated from a next subsequent eye exam included in the plurality of eye different eye examinations by a time gap, for each of the plurality of different eye examinations, the eye characteristic data including one or more measured sphere values for the sphere of the patient's eyes;
access demographic data for the patient;
input the eye characteristic data, time gaps, and demographic data into a predictive model, the eye characteristic data including, for each of the plurality of different eye examinations, the one or more measured sphere values for the sphere of the patient's eyes, the predictive model formulated from other patient data acquired during other eye examinations for a plurality of other patients, the other patient data including, for each other patient included in the plurality of other patients, one or more measured sphere values for the sphere of the other patient's eyes, other eye characteristic data for the other patient's eyes, and demographic data for the other patient, the predictive model transforming the eye characteristic data for the patient, the time gaps, and the demographic data for the patient through linear regression to:
forecast sphere values for the sphere of the patient's eyes at a plurality of different post examination time periods, the forecast sphere values inferred from the one or more measured sphere values for the sphere of the patient's eyes acquired during different eye examinations for the patient and from the demographic data for the patient in view of the time gaps separating each of the plurality of eye examinations; and
formulate a time series chart of sphere for the patient's eyes from the forecast sphere values, the time series chart forecasting likely sphere values for the sphere of the patient's eyes over a plurality of different time periods in the future; and
return the formulated time series chart as a prediction of changes to the sphere of the patient's eyes as the patient ages.
- 18. A computer system, the computer system comprising:
one or more hardware processors; system memory coupled to the one or more hardware processors, the system memory storing instructions that are executable by the one or more hardware processors; the one or more hardware processors executing the instructions stored in the system memory to predict progress of an eye condition for a patient's eyes, including the following:
access eye characteristic data for the patient's eyes, the eye characteristic data acquired during an eye examination performed on the patient, the eye characteristic data including one or more measured condition values for the eye condition of the patient's eyes;
access demographic data for the patient;
input the eye characteristic data, including the one or more measured condition values for the eye condition of the patient's eyes, and the demographic data into a predictive model in the system memory, the predictive model formulated from other patient data acquired during other eye examinations for a plurality of other patients, the predictive model transforming the eye characteristic data for the patient and the demographic data through linear regression to:
forecast condition values for the eye condition of the patient's eyes at a plurality of different post examination time periods, the forecast condition values inferred from the eye characteristic data for the patient, including the one or more measured condition values for the eye condition of the patient's eyes, and the demographic data for the patient; and
formulate a time series chart of eye condition for the patient's eyes from the forecast condition values, the time series chart forecasting likely condition values for the eye condition of the patient's eyes over a plurality of different time periods in the future;
return the formulated time series chart as a prediction of how the eye condition is to progress in the future.
