Systems and methods for computing risk of predicted medical outcomes in patients treated with multiple medications

A system for training a machine learning model for assessing risk of at least one predicted medical outcome for a patient having a plurality of patient-specific parameters and future planned treatment with a plurality of patient-specific medications, comprising:

• at least one hardware processor executing a code for:
• in a pre-processing step, for each of a plurality of sample patients:
• mapping a plurality of patient-specific medications and a plurality of patient-specific parameters to a plurality of risk scores of a plurality of medical outcomes by a mapping data structure that includes a plurality of relationship-mappings that map between a plurality of medications, a plurality of predicted medical outcomes, and a plurality of patient parameters, wherein each relationship-mapping includes at least one respective medication of the plurality of medications, at least one respective predicted medical outcome of the plurality of medical outcomes, at least one respective patient parameter of the plurality of patient parameters, and at least one respective risk score for the one respective predicted medical outcome,
• wherein at least one of the plurality of relationship-mappings includes a predicted medical outcome denoting a medication induced event of a medication of the plurality of medications, and at least one patient parameter selected from the group consisting of:
• a medication influencing factor directly affecting the medication which affects the medication induced event, and an event influencing factor directly affecting the medication induced event,
• wherein the medication influencing factor is selected from the group consisting of the following primary parameters affecting the medication:
• blood level, absorption, distribution, metabolism, elimination, half life elimination, and pharmacodynamics,
• wherein at least one patient-specific medication is mapped to a plurality of active ingredients, and wherein a subset of the plurality of relationship-mappings each include a respective active ingredient of the plurality of active ingredients, and the medication influencing factor is for the respective active ingredient;
• computing an aggregated risk score for each respective predicted medical outcome by aggregating the risk scores of the plurality of relationship-mappings including each respective predicted medical outcome identified by the mapping data-structure;
• and
• automatically designating the aggregated risk score as a ground truth label for a record corresponding to the sample patient for creating a training dataset for training a machine learning model;
• in a training step, creating the training dataset comprising a plurality of records for a plurality of sample patients, wherein a record of a sample patient is computed by:
• defining an input vector as a respective plurality of patient-specific medications and a respective plurality of patient-specific parameters of the sample patient mapped by the mapping data-structure, and
• designating an output vector as the ground truth label, the output vector including the aggregated risk score computed for each respective predicted medical outcome;
• and
• training a machine learning model on the training dataset using a supervised learning approach using the ground truth label, by iteratively teaching the machine learning model to infer a function that maps the input vector of each record of the training dataset to the corresponding ground truth label of the record,
• wherein the machine learning model is iteratively trained to learn the function that predicts a target aggregated risk score for each respective target predicted medical outcome in response to being fed a plurality of target patient-specific medications and a plurality of target patient-specific parameters for a target patient,
• wherein the machine learning model generates the predicted target aggregated risk score without accessing the mapping data-structure.

A method of assessing risk of at least one predicted medical outcome for a patient having a plurality of patient-specific parameters and future planned treatment with a plurality of patient-specific medications, comprising:

• feeding a plurality of target patient-specific medications and a plurality of target patient-specific parameters for a target patient into a machine learning model;
• and
• obtaining an outcome of a predicted target aggregated risk score for each one of a plurality of target predicted medical outcomes,
• wherein the machine learning model generates the predicted target aggregated risk score without accessing the mapping data-structure,
• wherein the machine learning model is trained on a training dataset using a supervised learning approach using ground truth labels, by iteratively teaching the machine learning model to iteratively infer a function that maps the input vector of each record of the training dataset to the corresponding ground truth label of the record,
• wherein the training set comprises:
• in a pre-processing step, for each of a plurality of sample patients:
• mapping a plurality of patient-specific medications and a plurality of patient-specific parameters to a plurality of risk scores of a plurality of medical outcomes by a mapping data structure that includes a plurality of relationship-mappings that map between a plurality of medications, a plurality of predicted medical outcomes, and a plurality of patient parameters, wherein each relationship-mapping includes at least one respective medication of the plurality of medications, at least one respective predicted medical outcome of the plurality of medical outcomes, at least one respective patient parameter of the plurality of patient parameters, and at least one respective risk score for the at least one respective predicted medical outcome,
• wherein at least one of the plurality of relationship-mappings includes a predicted medical outcome denoting a medication induced event of a medication of the plurality of medications, and at least one patient parameter selected from the group consisting of:
• a medication influencing factor directly affecting the medication which affects the medication induced event, and an event influencing factor directly affecting the medication induced event,
• wherein the medication influencing factor is selected from the group consisting of the following primary parameters affecting the medication:
• blood level, absorption, distribution, metabolism, elimination, half life elimination, and pharmacodynamics,
• wherein at least one patient-specific medication is mapped to a plurality of active ingredients, and wherein a subset of the plurality of relationship-mappings each include a respective active ingredient of the plurality of active ingredients, and the medication influencing factor is for the respective active ingredient;
• computing an aggregated risk score for each respective predicted medical outcome by aggregating the risk scores of the plurality of relationship-mappings including each respective predicted medical outcome identified by the mapping data-structure;
• automatically designating the aggregated risk score as a ground truth label for a record corresponding to the sample patient for creating a training dataset for training a machine learning model;
• in a training step, creating the training dataset comprising a plurality of records for a plurality of sample patients, wherein a record of a sample patient is computed by:
• defining an input vector as a respective plurality of patient-specific medications and a respective plurality of patient-specific parameters of the sample patient mapped by the mapping data-structure, and
• designating an output vector as the ground truth label, the output vector including the aggregated risk score computed for each respective predicted medical outcome.