System, medical item including RFID chip, data collection engine, server and method for capturing medical data
AssigneeBrain Trust Innovations I, LLC
InventorDavid LaBorde
Life of this patent
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Abstract
A system includes a plurality of RFID chips affixed to a catheter, a data collection engine device, and a server device. The data collection engine wirelessly transmits power to a first one of the RFID chips and receives first medical data from the first RFID chip while the first RFID chip is activated by the power receiver. The data collection engine generates a first message indicative of the first medical data to be sent to the server device. The server device can determine aspects of the catheter such as position and risk states based upon the first medical data.
Claims14
A server device comprising:
- a transceiver configured to receive a message from an item via a network connection, the message including identification information associated with an RFID chip associated with the item and a plurality of measured values;
- a controller operatively coupled to the transceiver;
- and
- one or more memory sources operatively coupled to the controller, the one or more memory sources storing instructions for configuring the controller to determine a parameter of the item based upon the plurality of measured values,
- wherein the one or more memory sources further store a trained model for generating an output value associated with the parameter based upon the plurality of measured values,
- wherein the trained model is a trained neural network model (NNM), and
- wherein the output value is a risk state associated with the item.
A server device comprising:
- a transceiver configured to receive a message from an item via a network connection, the message including identification information associated with an RFID chip associated with the item and a plurality of measured values;
- a controller operatively coupled to the transceiver;
- and
- one or more memory sources operatively coupled to the controller, the one or more memory sources storing instructions for configuring the controller to determine a parameter of the item based upon the plurality of measured values, wherein:
- the one or more memory sources further store a trained model for generating an output value associated with the parameter based upon the plurality of measured values;
- the one or more memory sources further store a plurality of past risk state events, each of the plurality of past risk state events including a plurality of item attributes and a quantifiable outcome;
- and
- the controller is further configured to:
- train a neural network model (NNM) to generate the trained model, wherein the training of the NNM includes:
- performing pre-processing on the plurality of item attributes for each of the plurality of past risk state events to generate a plurality of input data sets;
- dividing the plurality of past risk state events into a first set of training data and a second set of validation data;
- iteratively performing a machine learning algorithm (MLA) to update synaptic weights of the NNM based upon the training data;
- and
- validating the NNM based upon the second set of validation data.
A reader device comprising:
- an RFID antenna;
- a transceiver configured to receive first data from an RFID tag, the first data including identification information;
- a controller operatively coupled to the transceiver;
- and
- one or more memory sources operatively coupled to the controller, the one or more memory sources including instructions for configuring the controller to generate one or more messages indicative of the identification information to be sent by the transceiver to a server device via a network connection, wherein:
- the transceiver is further configured to receive second data from the RFID tag, the second data including a plurality of measured values;
- the controller is further configured to generate one or more messages indicative of the plurality of measured values to be sent by the transceiver to the server device to be input into a trained model to generate an output value associated with the item,
- the trained model is a trained neural network model (NNM), and
- the output value is one of a prediction value associated with a discontinuance risk or occurrence of infection.
Dependencies are inferred from the claim text.