Method and system for assessing drug efficacy using multiple graph kernel fusion

A system for computing a probable drug efficacy comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform:

• for a plurality of patients, generating and storing in the memory a directed acyclic graph as an extraction and transformation from electronic health records of each patient, the electronic health records comprising demographic information for each patient and medical event information for each patient, using the processor to extract demographic data and medical events data records from the electronic health records and to transform each extracted medical events data record into a node in a directed acyclic graph, each directed acyclic graph comprising a first node representing first demographic information of the patient, a plurality of additional nodes, each additional node representing a medical event of the patient, at least one first edge connecting the first node to an additional node, the first edge having a weight based on second demographic information of the patient, and a plurality of additional edges, each additional edge connecting nodes representing two consecutive medical events, the edge having a weight based on a time difference between the two consecutive medical events;
• capturing, using the processor, a plurality of features from each directed acyclic graph stored in the memory;
• training a neural network classification model using a plurality of the captured features of each directed acyclic graph;
• applying the neural network classification model to determine a probability that a drug or treatment will be effective for a particular patient;
• and
• determining, using the processor, a drug or treatment to be prescribed to the particular patient based on the determined probability.

A computer program product for computing a probable drug efficacy, the computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer, to cause the computer to perform a method comprising:

• for a plurality of patients, generating and storing in the memory a directed acyclic graph as an extraction and transformation from electronic health records of each patient, the electronic health records comprising demographic information for each patient and medical event information for each patient, using the processor to extract demographic data and medical events data records from the electronic health records and to transform each extracted medical events data record into a node in a directed acyclic graph each directed acyclic graph comprising a first node representing first demographic information of the patient, a plurality of additional nodes, each additional node representing a medical event of the patient, at least one first edge connecting the first node to an additional node, the first edge having a weight based on second demographic information of the patient, and a plurality of additional edges, each additional edge connecting nodes representing two consecutive medical events, the edge having a weight based on a time difference between the two consecutive medical events;
• capturing, using the processor, a plurality of features from each directed acyclic graph stored in the memory;
• training a neural network classification model using a plurality of the captured features of each directed acyclic graph;
• applying the neural network classification model to determine a probability that a drug or treatment will be effective for a particular patient;
• and
• determining, using the processor, a drug or treatment to be prescribed to the particular patient based on the determined probability.