Multimodal learning framework for analysis of clinical trials
AssigneeZasti Inc.
InventorsRamanathan Krishnan, John Domenech
Life of this patent
Estimating the term…
Abstract
A facility providing a medical outcome prediction model data structure is described. The data structure constitutes a trained statistical model that can be applied to image data and electronic health record data for a patient to predict a cancer survival outcome for the patient.
Claims19
A method for forecasting a clinical outcome for a subject patient based on a clinical trial involving a group of patients, comprising:
- obtaining patient image data for patients of the group;
- extracting features from the obtained patient image data for patients of the group;
- obtaining patient electronic health record data for patients of the group;
- extracting features from the obtained electronic health record data for patients of the group;
- for each patient of the group, concatenating the extracted features;
- obtaining a value of the clinical outcome for patients of the group;
- using the extracted features concatenated for each member of the group, and clinical outcome values obtained for each member of the group, training a random forest survival regression model that forecasts the clinical outcome;
- obtaining patient image data for the subject patient;
- extracting features from the obtained patient image data for the subject patient;
- obtaining patient electronic health record data for the subject patient;
- extracting features from the obtained electronic health record data for the subject patient;
- concatenating the extracted features for the subject patient;
- and
- applying the random forest survival regression model to the concatenated extracted features for the subject patient to obtain a prediction of the clinical outcome for the subject patient.
One or more storage devices collectively storing a medical outcome prediction model data structure, the data structure comprising:
- a trained statistical model that can be applied to image data and electronic health record data for a patient to predict a cancer survival outcome for the patient.
One or more instances of computer-readable media collectively having contents configured to cause a computing system to perform a method for predicting a clinical outcome for a subject patient based on a clinical trial involving a group of patients, the method comprising:
- obtaining patient image data for patients of the group;
- extracting features from the obtained patient image data for patients of the group;
- obtaining patient electronic health record data for patients of the group;
- extracting features from the obtained electronic health record data for patients of the group;
- for each patient of the group, concatenating the extracted features;
- obtaining a value of the clinical outcome for patients of the group;
- using the extracted features concatenated for each member of the group, and clinical outcome values obtained for each member of the group, training a statistical model to predict the clinical outcome;
- obtaining patient image data for the subject patient;
- extracting features from the obtained patient image data for the subject patient;
- obtaining patient electronic health record data for the subject patient;
- extracting features from the obtained electronic health record data for the subject patient;
- concatenating the extracted features for the subject patient;
- and
- applying the random forest survival regression model to the concatenated extracted features for the subject patient to obtain a prediction of the clinical outcome for the subject patient.
Dependencies are inferred from the claim text.