Non-Invasive pRBC Hb and Iron Prediction for Transfusion Matching

The patent replaces complex physical measurement systems with a computational machine learning model. Instead of using invasive physical methods (liver biopsy, MRI) or complex in-vitro assays to directly measure Hb and iron content, the system uses algorithms trained on donor data to predict these values non-invasively, substituting mechanical/physical measurement complexity with computational complexity.

The patent creates a virtual copy of the physical measurement process through machine learning models. The models learn the relationship between donor characteristics (fingertip Hb, sex, age) and actual pRBC Hb/iron content from training data, then replicate this relationship to predict values for new units without physical measurement, effectively copying the measurement function through computational means.

The patent implements feedback through the training and validation process. The machine learning models are trained on validated data where actual pRBC Hb and iron content are known (from quality control measurements), allowing the models to learn accurate relationships and continuously improve their predictions. This feedback loop ensures that non-invasive predictions achieve high precision by being calibrated against ground truth data.

The patent performs preliminary actions by collecting and storing donor characteristics and quality control measurements during the blood collection and processing phase. This preliminary data accumulation creates a foundation for later accurate predictions, allowing the system to determine Hb and iron content accurately before the pRBC units are released for transfusion, without requiring invasive testing at the point of use.

The patent performs the time-consuming data collection, model training, and validation actions during the blood collection and processing phase, before the pRBC units are ready for transfusion. By completing these preliminary computational tasks in advance using existing donor data and quality control measurements, the system avoids adding time delays at the critical transfusion decision point while still achieving individual unit precision.

The patent enables the system to self-determine Hb and iron content using its own stored data and trained models, without requiring external laboratory testing or invasive procedures. The machine learning system serves itself by automatically predicting values based on already-available donor characteristics and production data, eliminating the need for additional manual measurement processes that would consume time.