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

• US 10,783,991 B1
• Filed: 08/14/2019
• Issued: 09/22/2020
• 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): Khan, Omer S
• Application Number:
  US16/541,106
• Time in Patent Office: 405 Days
• Field of Search: 340 101
• CPC Class Codes: A61B 2562/08 Sensors provided with means for identification, e.g. barcodes or memory chips
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: G06K 19/0723 the record carrier comprising an arrangement for non-contact communication, e.g. wireless communication circuits on transponder cards, non-contact smart cards or RFIDs
• CPC Class Codes: G06K 19/07762 the adhering arrangement making the record carrier wearable, e.g. having the form of a ring, watch, glove or bracelet record carriers for insertion in the human body for medical purposes A61B90/00; record carriers adapted for attachment to animals A01K11/00
• CPC Class Codes: G06K 7/10366 the interrogation device being adapted for miscellaneous applications
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/006 based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/0409 Adaptive resonance theory [ART] networks
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/048 Activation functions
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 3/086 using evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/041 Abduction
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/0633 : Workflow analysis
• CPC Class Codes: G06Q 10/08 : Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 50/22 : Social work or social welfare, e.g. community support activities or counselling services
• 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 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

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.

Claims

1. 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 RFID chip received power wirelessly from a reader device,

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 one of a prediction value associated with a discontinuance risk or occurrence of infection.

2. The server device of claim 1, wherein the controller is further configured to determine an air-body interface of the item based upon the measured values.

3. The server device of claim 1, wherein the controller is further configured to determine an insertion time at which the item was inserted in a human body cavity and a discontinuance time at which the item became removed from the human body cavity based upon the identification information and the measured values.

4. The server device of claim 1, wherein:

the controller is further configured to;

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

generate the output value from the trained model based upon the input data set.

8. The server device of claim 1, wherein the transceiver configured to receive the message from a reader device via the network connection, the reader device comprising a power transmission subsystem including a power source arranged to wirelessly transmit power to the RFID chip associated with the item via an antenna.

9. The server device of claim 8, wherein the reader device is configured to communicate with RAIN ISO 18000-6C, EPC Class 1 Gen2 compliant RFID chips.

10. The server device of claim 8, wherein the reader device is configured to operate in 902MHz-928MHz, 920 MHz-925MHz and 860 MHz-868MHz frequency ranges.

5. A reader device comprising:

an RFID antenna;

a power transmission subsystem including a power source and an antenna arranged to wirelessly transmit power from the power source to an RFID tag associated with an item;

a transceiver configured to receive first data from the 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.

6. The reader device of claim 5, wherein the transceiver is configured to communicate with RAIN ISO 18000-6C, EPC Class 1 Gen2 compliant RFID tags.

7. The reader device of claim 5, wherein the transceiver is configured to operate in 902 MHz-928 MHz, 920 MHz-925 MHz and 860 MHz-868 MHz frequency ranges.

11. 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 RFID chip received power wirelessly from a reader device;

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.

12. The server device of claim 11, 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;

the sigmoid function ƒ

(x)=1/(1+e<sup>−

x</sup>);

 

 

(1)

the hyperbolic tangent function ƒ

(x)=(e^2x−

1)/(e^2x+1) and 

 

(2)

a linear function ƒ

(x)=x, 

 

(3)

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

13. The server device of claim 11, wherein the NNM is one or more of a feed forward structure Neural Network;

ADALINE Neural Network, Adaptive Resonance Theory 1 (ART1), Bidirectional Associative Memory (BAM), Boltzmann Machine, Counterpropagation Neural Network (CPN), Elman Recurrent Neural Network, Hopfield Neural Network, Jordan Recurrent Neural Network, Neuroevolution of Augmenting Topologies (NEAT), and Radial Basis Function Network.

14. The server device of claim 11, wherein the plurality of past risk state events includes catheter discontinuance from insertion in human cavity and catheter related infection occurrence, the plurality of item attributes includes the measured values, location along a catheter where said measured values were obtained, and time values associated with catheter insertion and removal.