System, medical item including RFID chip, data collection engine, server and method for capturing medical data

• US 10,028,707 B1
• Filed: 03/20/2018
• Issued: 07/24/2018
• Est. Priority Date: 02/06/2015
• Status: Active Grant

• Current Assignee: Brain Trust Innovations I, LLC
• Original Assignee: Brain Trust Innovations I, LLC
• Inventors: LaBorde, David
• Primary Examiner(s): BLOUIN, MARK S
• Application Number:
  US15/926,251
• Publication Number:
  US 20180206794A1
• Time in Patent Office: 126 Days
• Field of Search: 340 101
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 2562/08 Sensors provided with means for identification, e.g. barcodes or memory chips
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/065 Determining position of the probe employing exclusively positioning means located on or in the probe, e.g. using position sensors arranged on the probe
• CPC Class Codes: A61B 5/6852 Catheters
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06K 19/07758 arrangements for adhering the record carrier to further objects or living beings, functioning as an identification tag
• CPC Class Codes: G06K 7/10366 the interrogation device being adapted for miscellaneous applications
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G16H 10/65 stored on portable record carriers, e.g. on smartcards, RFID tags or CD
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A system includes a plurality of RFID chips affixed to a medical item, 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 medical item such as position and risk states based upon the first medical data.

Claims

1. A system comprising:

a sensor device including a plurality of sensors configured to sense a plurality of values associated with a patient, and a radio-frequency identification (RFID) chip, wherein the RFID chip includes;

a controller configured according to instructions in memory to generate a message including an identification information associated with the RFID chip and the plurality of sensed values; and

an antenna portion configured to transmit the message;

a data collection engine (DCE) communicating with the RFID chip, wherein the DCE comprises;

a power transmission subsystem including a power source and an antenna arranged to wirelessly transmit power from the power source to the RFID chip;

a transceiver configured to receive the message from the RFID chip;

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 and the plurality of sensed values to be sent by the transceiver to a server device via a network connection,

wherein the RFID chip includes an antenna for wirelessly receiving the power from the transceiver of the DCE;

wherein the server device comprises;

a transceiver configured to receive the one or more messages from the DCE;

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;

a first database including a plurality of risk states and patient attributes,

a trained neural network model (NNM) for generating an output value based upon the plurality of sensed values; and

instructions for configuring the controller to determine whether the output value corresponds with one or more of the patient attributes associated with one or more of the risk states in the first database.

2. The system of claim 1, wherein the controller of the server device is further configured to:

perform pre-processing on the plurality of sensed values to generate an input data set;

generate the output value from the trained neural network model based upon the input data set; and

wherein the plurality of risk states includes one of incorrect position risk, a pressure ulcer risk, and a falling risk.

3. The system of claim 1, wherein in the server device:

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 patient attributes and a quantifiable outcome; and

the controller is further configured to;

train the neural network model (NNM) to generate the trained NNM, wherein the training of the NNM includes;

performing pre-processing on the plurality of patient 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 first set of training data; and

validating the NNM based upon the second set of validation data.

4. The system of claim 3, wherein:

the NNM includes an input layer, output layer, and a plurality of hidden layers with a plurality of hidden neurons; and

each of the plurality of hidden neurons includes an activation function, the activation function is one of;

(1) a sigmoid function f(x)=1/(1+e<sup>−

x</sup>);

(2) a hyperbolic tangent function f(x)=(e^2x−

1)/(e^2x+1); and

(3) a linear function f(x)=x,

wherein x is a summation of input neurons biased by the synaptic weights.

5. The system of claim 1, wherein:

at least one of the plurality of sensors is a pressure sensor;

at least one of the plurality of sensors is a temperature sensor;

at least one of the plurality of sensors is a moisture sensor; and

the server device is further configured to generate an event message based upon the determination of whether the output value corresponds with the one or more of the patient attributes associated with one or more of the risk states in the first database.

6. The system of claim 1, wherein the controller of the server device is further configured to:

perform pre-processing on the plurality of sensed values and the identification information to generate an input data set;

generate the output value from the trained neural network model based upon the input data set; and

wherein the plurality of risk states includes pressure ulcer infection risk.

7. A sensor device comprising:

an RFID chip; and

a plurality of sensors sensing values associated with a patient,

wherein the RFID chip includes;

a controller configured according to instructions in memory to generate a message including identification information associated with the RFID chip and the plurality of sensed values; and

a transmitting portion configured to send the message to a remote entity.

8. The sensor device according to claim 7, further comprising a pressure sensor configured to sense a pressure value associated with the patient, and wherein the message further includes the pressure value.

9. The sensor device according to claim 7, wherein the message further includes a position signature of the patient.

10. The sensor device according to claim 7, further comprising a plurality of moisture sensors sensing a plurality of moisture values, and wherein the message further includes the plurality of moisture values.

11. The sensor device according to claim 7, wherein the sensor device is configured to be affixed adhesively to the patient.

12. A server device comprising:

a transceiver configured to receive a message from a sensor device via a network connection, the message including identification information associated with an RFID chip associated with the sensor device and a plurality of sensed 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 position of a patient based upon the plurality of sensed values.

13. The server device of claim 12, wherein the controller is further configured to determine a placement time at which an orientation of patient changed.

14. The server device of claim 12, wherein:

the one or more memory sources further store a trained neural network model (NNM) for generating an output value associated based upon the plurality of sensed values, a start time for each of the plurality of sensed values, and an end time for each of the plurality of sensed values; and

the output value is a prediction value associated with a pressure ulcer risk.

15. The server device of claim 14, wherein:

the controller is configured to generate a message including the prediction value; and

the transceiver is configured to send the message to a remote entity.

16. The server device of claim 12, wherein the one or more memory sources further store a trained neural network model (NNM) for generating an output value associated based upon the plurality of sensed values, wherein the output value is a prediction value associated with a falling risk.

17. The server device of claim 16, wherein:

the controller is configured to generate a message including the prediction value; and

transceiver is configured to send the message to a remote entity.

18. The server device of claim 12, wherein:

the one or more memory sources further store a trained neural network model (NNM) for generating an output value based upon the plurality of sensed values;

the controller is further configured to;

perform pre-processing on the plurality of sensed values to generate an input data set;

generate the output value from the trained neural network model based upon the input data set;

wherein the output value is a prediction value associated with a pressure ulcer related infection risk; and

generate a message including the prediction value,

wherein the transceiver is configured to send the message to a remote entity.

19. The system of claim 12, wherein in the server device:

the one or more memory sources further store a plurality of past state events, each of the plurality of past state events including a plurality of patient attributes and a quantifiable outcome; and

the controller is further configured to;

train a neural network model (NNM) to generate the trained NNM, wherein the training of the NNM includes;

performing pre-processing on the plurality of patient attributes for each of the plurality of past 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 first set of training data; and

validating the NNM based upon the second set of validation data.

20. The server device of claim 12, wherein:

the controller is configured to generate a message including the position of a patient; and

the transceiver is configured to send the message to a remote entity to be rendered on the display of the remote entity.