Dual antiplatelet therapy and time based risk prediction

At least one computer readable storage medium comprising a set of instructions, which when executed by a computing system, cause the computing system to:

• identify a set of preoperative baseline characteristics associated with a procedure on a pooled patient population;
• determine, by a machine learning model, a set of health failure probabilities for a target patient based on the set of preoperative baseline characteristics and a set of preoperative target characteristics, wherein the set of preoperative target characteristics correspond to the target patient, and wherein the set of preoperative baseline characteristics and a number of characteristics in the set of preoperative baseline characteristics yield area under the curve (AUC) values of greater than 0.8 by decision tree procedure in the machine learning model;
• and
• pair, by the machine learning model, each probability in the set of health failure probabilities with a postoperative dual antiplatelet therapy (DAPT) duration for the target patient.

A method comprising:

• identifying a set of preoperative baseline characteristics associated with a procedure on a pooled patient population;
• determining, by a machine learning model, a set of health failure probabilities for a target patient based on the set of preoperative baseline characteristics and a set of preoperative target characteristics, wherein the set of preoperative target characteristics correspond to the target patient, and wherein the set of preoperative baseline characteristics and a number of characteristics in the set of preoperative baseline characteristics yield area under the curve (AUC) values of greater than 0.8 by decision tree procedure in the machine learning model;
• and
• pairing, by the machine learning model, each probability in the set of health failure probabilities with a postoperative dual antiplatelet therapy (DAPT) duration for the target patient.

A computing system comprising:

• a processor;
• and
• a memory coupled to the processor, the memory including a set of instructions, which when executed by the processor, cause the computing system to:
• generate, by a machine learning model, a Shapley plot based on relative importance of a group of patients to a plurality of variables;
• conduct a conversion of a portion of the Shapley plot into a hazard ratio value, wherein the hazard ratio value is a single value corresponding to a first variable in the plurality of variables;
• and
• generate a hazard ratio plot based at least in part on the hazard ratio value.

At least one computer readable storage medium comprising a set of instructions, which when executed by a computing system, cause the computing system to:

• generate, by a machine learning model, a Shapley plot based on relative importance of a group of patients to a plurality of variables;
• conduct a conversion of a portion of the Shapley plot into a hazard ratio value, wherein the hazard ratio value is a single value corresponding to a first variable in the plurality of variables;
• and
• generate a hazard ratio plot based at least in part on the hazard ratio value.

A method comprising:

• generating, by a machine learning model, a Shapley plot based on relative importance of a group of patients to a plurality of variables;
• conducting a conversion of a portion of the Shapley plot into a hazard ratio value, wherein the hazard ratio value is a single value corresponding to a first variable in the plurality of variables;
• and
• generating a hazard ratio plot based at least in part on the hazard ratio value.