Abstract: Techniques for patient data management include obtaining medical history data of patients from a database, the medical history data including a history of one or more diagnosis events, one or more treatment events, and a clinical outcome event for each patient of the patients. For each patient, based on the medical history data, a computing system generates a patient pathway that includes a graph including nodes of one or more diagnosis events, the one or more treatment events, and the clinical outcome event. The computing system receives, via an interface, one or more criteria to select a subset of the patient pathways of the patients. The computing system selects, based on the one or more criteria, graphs representing the subset of the patient pathways. The computing system aggregates the subset of the patient pathways into a merged graph. The computing system displays the merged graph in the interface.

Abstract: A machine learning model for traversing a decision tree, the machine learning model trained from a structured data set including a first set of key-value pairs and subject-specific criteria using the key-value pairs. The first set of key-value pairs is transformed into a second set of key-value pairs, which are projected to a subject-specific point within a multi-dimensional space. The decision tree includes decision and leaf nodes. Each leaf node is connected to a root node via a leaf-node-specific trajectory. Each decision node corresponds to a criterion using a value in the second set of key-value pairs. For each leaf node, a leaf-node-specific point within the multi-dimensional space is determined using the leaf-node-specific trajectory, and a similarity score is determined using the leaf-node-specific and subject-specific points. A subset of the leaf nodes is identified using the scores. State or protocol information for each leaf node in the subset is retrieved.