Interoperable privacy-preserving multi-center distributed machine learning method for healthcare applications

A non-transitory computer readable medium comprising stored instructions, the stored instructions when executed by at least one processor of one or more computing devices, cause the one or more computing devices to:

• provide a data conversion module for deployment in a set of client nodes, the data conversion module when installed on client node configured to generate a set of components, the set of components including at least a pre-assessment tool and a data conversion model for performing one or more conversion operations on client data associated with the client node;
• provide, to each client node, a base model to the client node for training by the client node using the client data;
• receive, from the pre-assessment tool on each client node, statistics and abstract information describing the client data for the client node;
• generate, for each client node, conversion logic based on the received statistics and abstract information on the client data for the client node, wherein the conversion logic includes instructions for converting values of the client data for one or more data fields to match a target schema for training the base model;
• provide, to each client node, the conversion logic to the data conversion model of the client node;
• and
• receive, from each client node, trained parameters of the base model from the client node, wherein the parameters of the base model are trained using converted values by executing the data conversion model to perform one or more conversion operations using the conversion logic.

A computer system comprising:

• one or more computer processors;
• and
• one or more computer readable mediums storing instructions that, when executed by the one or more computer processors, cause the computer system to:
• provide a data conversion module for deployment in a set of client nodes, the data conversion module when installed on client node configured to generate a set of components, the set of components including at least a pre-assessment tool and a data conversion model for performing one or more conversion operations on client data associated with the client node;
• provide, to each client node, a base model to the client node for training by the client node using the client data;
• receive, from the pre-assessment tool on each client node, statistics and abstract information describing the client data for the client node;
• generate, for each client node, conversion logic based on the received statistics and abstract information on the client data for the client node, wherein the conversion logic includes instructions for converting values of the client data for one or more data fields to match a target schema for training the base model;
• provide, to each client node, the conversion logic to the data conversion model of the client node;
• and
• receive, from each client node, trained parameters of the base model from the client node, wherein the parameters of the base model are trained using converted values by executing the data conversion model to perform one or more conversion operations using the conversion logic.