Blood transfusion management using artificial intelligence analytics

A system, comprising:

• a memory that stores computer executable components;
• a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise:
• a reception component that receives patient information for a patient that is a potential candidate for a transfusion procedure, wherein the patient information comprises clinical condition information regarding a current clinical condition of the patient, and medical history information regarding a medical history of the patient;
• an outcomes forecasting component that employs one or more first machine learning processes to forecast expected outcomes of the transfusion procedure based on the patient information, historical transfusion data for other patients associated with a similar clinical context of the patient, and domain knowledge transfusion, wherein the expected outcomes include potential adverse outcomes and likelihood of occurrence of the adverse outcomes, and
• wherein the one or more first machine learning processes comprise:
• generating a feature vector representation for the patient information using a dimensionality reduction model;
• identifying the subset of similar patients to the patient included in the historical transfusion data based on matching the feature vector representation with reference feature vectors generated for the similar patients using the dimensionality reduction model, and
• evaluating historical outcomes for the similar patients;
• and
• a transfusion appropriateness analysis component that employs one or more second machine learning processes to determine a measure of appropriateness of the transfusion procedure for the patient based on the expected outcomes, the patient information, the historical transfusion data for the other patients and the domain knowledge transfusion data.