Decision support tools for reducing readmissions of individuals with acute myocardial infarction

A system having one or more hardware processors configured to facilitate a plurality of operations, the operations comprising:

• receiving an indication, associated with a current encounter, that a patient is an acute myocardial infarction (AMI) patient who is having or has had an AMI;
• identifying (a) a first time interval and a second time interval in relation to patient treatment associated with AMI and (b) the patient as an AMI patient, wherein the first time interval and the second time interval differ;
• collecting, via the one or more hardware processors, electronic encoded information specifying content associated with an AMI patient identifier, the patient, and AMI biometric patient data, wherein:
• the one or more hardware processors are associated with an electronic memory at a medical records computer system,
• the AMI biometric patient data is generated via a sensor device configured to indicate a measurement metric of an AMI condition corresponding to the patient, and
• the AMI biometric patient data is associated with the AMI patient identifier and the electronic memory at the medical records computer system;
• in response to the receiving of the indication, automatically querying an electronic health record for the patient for a first plurality of feature values corresponding to the first time interval and a second plurality of feature values corresponding to the second time interval, at least one feature value of the first plurality of feature values (a) differing from any feature value of the second plurality of feature values and (b) being associated with the AMI biometric patient data;
• applying a first machine-learning electronic model to information associated with the first plurality of feature values to generate a first prediction, the first machine-learning electronic model trained on a first set of AMI patient data, associated with the first time interval, based on a first set of feature data, the first set of feature data generated by utilizing a feature selection algorithm comprising:
• (i) stratifying and/or segregating reference data to produce results containing a set of continuous features and a set of categorical features;
• and
• (ii) using the results with an extreme gradient boosting algorithm and an adaptive elastic-net algorithm to produce the first set of feature data;
• applying a second machine-learning electronic model to information associated with the second plurality of feature values to generate a second prediction, the second machine-learning electronic model trained on a second set of AMI patient data, associated with the second time interval, based on a second set of feature data generated by utilizing the feature selection algorithm;
• based on the first prediction and the second prediction, generating a readmission prediction of the patient being readmitted to a healthcare facility for AMI within a future time interval;
• and
• electronically writing, via the one or more hardware processors, electronic encoded data to the electronic memory at the medical records computer system, wherein the electronic encoded data indicates the readmission prediction.

A computer-implemented method, comprising:

• receiving an indication, associated with a current encounter, that a patient is an acute myocardial infarction (AMI) patient who is having or has had an AMI;
• identifying (a) a first time interval and a second time interval in relation to patient treatment associated with AMI and (b) the patient as an AMI patient, wherein the first time interval and the second time interval differ;
• collecting, via one or more hardware processors, electronic encoded information specifying content associated with an AMI patient identifier, the patient, and AMI biometric patient data, wherein:
• the one or more hardware processors are associated with an electronic memory at a medical records computer system,
• the AMI biometric patient data is generated via a sensor device configured to indicate a measurement metric of an AMI condition corresponding to the patient, and
• the AMI biometric patient data is associated with the AMI patient identifier and the electronic memory at the medical records computer system;
• in response to the receiving of the indication, automatically querying an electronic health record for the patient for a first plurality of feature values corresponding to the first time interval and a second plurality of feature values corresponding to the second time interval, at least one feature value of the first plurality of feature values (a) differing from any feature value of the second plurality of feature values and (b) being associated with the AMI biometric patient data;
• applying a first machine-learning electronic model to information associated with the first plurality of feature values to generate a first prediction, the first machine-learning electronic model trained on a first set of AMI patient data, associated with the first time interval, based on a first set of feature data, the first set of feature data generated by utilizing a feature selection algorithm comprising:
• (i) stratifying and/or segregating reference data to produce results containing a set of continuous features and a set of categorical features;
• and
• (ii) using the results with an extreme gradient boosting algorithm and an adaptive elastic-net algorithm to produce the first set of feature data;
• applying a second machine-learning electronic model to information associated with the second plurality of feature values to generate a second prediction, the second machine-learning electronic model trained on a second set of AMI patient data, associated with the second time interval, based on a second set of feature data generated by utilizing the feature selection algorithm;
• based on the first prediction and the second prediction, generating a readmission prediction of the patient being readmitted to a healthcare facility for AMI within a future time interval;
• and
• electronically writing, via the one or more hardware processors, electronic encoded data to the electronic memory at the medical records computer system, wherein the electronic encoded data indicates the readmission prediction.