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

• US 9,848,827 B1
• Filed: 05/10/2017
• Issued: 12/26/2017
• 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:
  US15/592,116
• Time in Patent Office: 230 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 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 system comprising:

a catheter including an elongated flexible tube for being inserted into a human body cavity, a plurality of radio-frequency identification (RFID) chips, and a temperature sensor sensing a plurality of temperature values along a long axis of the catheter, wherein at least a first RFID chip of the plurality of RFID chips is a passive-type RFID chip, wherein at least one of the first RFID chip and a second RFID chip of the plurality of RFID chips includes;

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

an antenna portion configured to transmit the message;

a data collection engine (DCE) communicating with the at least one of the first RFID chip and the second 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 first RFID chip;

a transceiver configured to receive the message from the at least one of the first RFID chip and the second 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 temperature values to be sent by the transceiver to a server device via a network connection,

wherein the first 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 catheter risk states and catheter attributes for each of the plurality of catheter risk states;

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

instructions for configuring the controller to determine whether the output value corresponds with one or more of the catheter attributes associated with one or more of the catheter 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 temperature values to generate an input data set;

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

determine whether the output value corresponds with a catheter attribute associated with a high risk of discontinuance as the catheter risk state.

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

the one or more memory sources further store a plurality of past catheter risk state events, each of the plurality of past catheter risk state events including a plurality of catheter 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 catheter attributes for each of the plurality of past catheter risk state events to generate a plurality of input data sets;

dividing the plurality of past catheter 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,

wherein the MLA for updating the synoptic weights is one or more of ADALINE training, backpropagation algorithm, competitive learning, genetic algorithm training, Hopfield learning, Instar and Outstar training, the Levenberg-Marquardt algorithm (LMA), Manhattan Update Rule Propagation, Nelder Mead Training, Particle Swarm (PSO) training, quick propagation algorithm, resilient propagation (RPROP) algorithm, scaled conjugate gradient (SCG), support vector machines, genetic programming, Bayesian statistics, decision trees, case based reasoning, information fuzzy networks, clustering, hidden Markov models, particle swarm optimization, simulated annealing.

4. The server device 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) the sigmoid function f(x)=1/(1+e<sup>−

x</sup>);

(2) the 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 synoptic weights.

5. The server device of claim 3, 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.

6. A catheter including an elongated flexible tube for being inserted into a human body cavity, the catheter comprising:

a plurality of radio-frequency identification (RFID) chips; and

a temperature sensor sensing a plurality of temperature values associated with the catheter,

wherein at least one of the plurality of RFID chips includes;

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

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

7. The catheter according to claim 6, further comprising a plurality of linearly oriented pressure sensors along a long axis of the elongated tube,

wherein the temperature sensor is a continuous temperature sensor disposed along a longest axis of the elongated tube, and

wherein the message further includes pressure values sensed by the plurality of linearly oriented pressure sensors.

8. The catheter according to claim 6, the plurality of temperature values sensed by the temperature sensor include:

(i) a temperature gradient along a long axis of the elongated tube; and

(ii) a temperature value at a most distal aspect of the catheter.

9. A server device comprising:

a transceiver configured to receive a message from a catheter via a network connection, the message including identification information associated with an RFID chip associated with the catheter and a plurality of sensed temperature values or pressure 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 depth of insertion of the catheter into the human body cavity based upon the sensed temperature values.

10. The server device of claim 9, wherein the controller is further configured to determine an air-body interface of the catheter based upon the sensed temperature values.

11. The server device of claim 9, wherein the controller is further configured to determine an insertion time at which the catheter was inserted in the human body cavity and a discontinuance time at which the catheter became removed from the human body cavity based upon the identification information and the plurality of sensed temperature and/or pressure values.

12. The server device of claim 9, wherein the message received by the transceiver further includes a plurality of sensed pressure values, and the controller determines the depth of insertion of the catheter into the human body cavity based upon the sensed temperature values and the sensed pressure values.

13. The server device of claim 9, 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 temperature values, wherein the output value is one of a prediction value associated with catheter discontinuance risk or occurrence of catheter related infection.

14. The server device of claim 9, 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 temperature values;

the controller is further configured to;

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

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

determine whether the output value corresponds with a catheter attribute associated with a high risk of discontinuance.

15. The system of claim 9, wherein in the server device:

the one or more memory sources further store a plurality of past catheter risk state events, each of the plurality of past catheter risk state events including a plurality of catheter 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 catheter attributes for each of the plurality of past catheter risk state events to generate a plurality of input data sets;

dividing the plurality of past catheter 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,

wherein the MLA for updating the synoptic weights is one or more of ADALINE training, backpropagation algorithm, competitive learning, genetic algorithm training, Hopfield learning, Instar and Outstar training, the Levenberg-Marquardt algorithm (LMA), Manhattan Update Rule Propagation, Nelder Mead Training, Particle Swarm (PSO) training, quick propagation algorithm, resilient propagation (RPROP) algorithm, scaled conjugate gradient (SCG), support vector machines, genetic programming, Bayesian statistics, decision trees, case based reasoning, information fuzzy networks, clustering, hidden Markov models, particle swarm optimization, simulated annealing.

16. The server device of claim 15, 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) the sigmoid function f(x)=1/(1+e<sup>−

x</sup>);

(2) the 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 synoptic weights.

17. The server device of claim 15, 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.

18. The server device of claim 15, wherein the plurality of past catheter risk state events includes catheter discontinuance from insertion in human cavity and catheter related infection occurrence, the plurality of catheter attributes includes measured temperature and pressure values, location along the catheter where said temperature and pressure values were obtained, and time values associated with catheter insertion and removal.