Abstract: Churn-aware training of a classifier which reduces the difference between predictions of two different models, such as a prior generation of a classification model and a subsequent generation. A second dataset of labelled data is scored on a prior generation of a classification model, wherein the prior generation was trained on a first dataset of labelled data. A subsequent generation of a classification model is trained with the second dataset of labelled data, wherein in training of the subsequent generation, weighting of at least some of the labelled data in the second dataset, such as labelled data threat yielded an incorrect classification, is adjusted based on the score of such labelled data in the prior generation.

Abstract: A system and method for tissue perfusion to assess, maintain, mature, and possibly rehabilitate the tissue. The system of the present teachings includes a tissue enclosure having a fluid reservoir. Pumps, valves, and a controller move perfusate through the tissue. The system includes features to assist in monitoring the health of the tissue, and a removable tray to facilitate moving the tissue from a point of origin to the tissue enclosure. The system moves perfusate to and through the tissue, and provides nutrition to the tissue. The system includes an output flow rate/volume sensor, at least one infusion pump, disposable and durable parts, and a sensor suite.

Abstract: Churn-aware training of a classifier which reduces the difference between predictions of two different models, such as a prior generation of a classification model and a subsequent generation. A second dataset of labelled data is scored on a prior generation of a classification model, wherein the prior generation was trained on a first dataset of labelled data. A subsequent generation of a classification model is trained with the second dataset of labelled data, wherein in training of the subsequent generation, weighting of at least some of the labelled data in the second dataset, such as labelled data threat yielded an incorrect classification, is adjusted based on the score of such labelled data in the prior generation.

Abstract: Churn-aware training of a classifier which reduces the difference between predictions of two different models, such as a prior generation of a classification model and a subsequent generation. A second dataset of labelled data is scored on a prior generation of a classification model, wherein the prior generation was trained on a first dataset of labelled data. A subsequent generation of a classification model is trained with the second dataset of labelled data, wherein in training of the subsequent generation, weighting of at least some of the labelled data in the second dataset, such as labelled data threat yielded an incorrect classification, is adjusted based on the score of such labelled data in the prior generation.

Abstract: System and method for maintaining an organ and transporting the organ from a donor to a recipient, maintaining normothermic temperature of the organ. The system includes a circulation system providing oxygenated perfusate to the organ and enabling continuous monitoring of the perfusate moving through the organ. The tank can provide an inlet that can accept selected infusible materials, and an outlet that can enable sampling and waste removal. The system can continually monitor the solution bath in which the organ is bathed, and the system can manage the composition of the gas in the tank.