Systems and Methods for Configuring a Task-Specific Machine-Learning Model at a Computer System

A computing system, comprising:

• control circuitry;
• memory;
• and
• one or more sets of instructions stored in the memory and configured for execution by the control circuitry, the one or more sets of instructions comprising instructions for:
• receiving a request from a user to modify a machine-learning model that is configured to perform a specific clinical task;
• retrieving, based on the machine-learning model, a corresponding node architecture defining a conditional logic for performing the specific clinical task by the machine-learning model, wherein:
• the conditional logic is executed in accordance with a first order of a first set of interconnected nodes from a plurality of nodes,
• the first order includes an input node, at least one output node, and an intermediate node disposed between the input node and the at least output node, and
• the first set of interconnected nodes comprises one or more data source nodes, one or more machine-learning model nodes, and one or more conditional logic nodes;
• generating, for display at a remote device, a representation of the corresponding node architecture, wherein the representation represents a plurality of input features including:
• a first input feature for configuring the conditional logic of the corresponding node architecture, and
• a second input feature for configuring a parameter of a corresponding node in the first set of interconnected nodes;
• receiving a selection of either the first input feature or the second input feature, wherein the selection of either the first input feature or the second input feature defines a second order of a second set of interconnected nodes from the plurality of nodes;
• and
• updating the conditional logic of the corresponding node architecture in accordance with the second order of the second set of interconnected nodes, thereby configuring how the machine-learning model performs the specific clinical task.

A non-transitory computer-readable storage medium storing one or more sets of instructions configured for execution by a computing device having control circuitry and memory, the one or more sets of instructions comprising instructions for:

• receiving a request from a user to modify a machine-learning model that is configured to perform a specific clinical task;
• retrieving, based on the machine-learning model, a corresponding node architecture defining a conditional logic for performing the specific clinical task by the machine-learning model, wherein:
• the conditional logic is executed in accordance with a first order of a first set of interconnected nodes from a plurality of nodes,
• the first order includes an input node, at least one output node, and an intermediate node disposed between the input node and the at least output node, and
• the first set of interconnected nodes comprises one or more data source nodes, one or more machine-learning model nodes, and one or more conditional logic nodes;
• generating, for display at a remote device, a representation of the corresponding node architecture, wherein the representation represents a plurality of input features including:
• a first input feature for configuring the conditional logic of the corresponding node architecture, and
• a second input feature for configuring a parameter of a corresponding node in the first set of interconnected nodes;
• receiving a selection of either the first input feature or the second input feature, wherein the selection of either the first input feature or the second input feature defines a second order of a second set of interconnected nodes from the plurality of nodes;
• and
• updating the conditional logic of the corresponding node architecture in accordance with the second order of the second set of interconnected nodes, thereby configuring how the machine-learning model performs the specific clinical task.