System, server and method for preventing suicide

• US 10,388,410 B1
• Filed: 07/08/2018
• Issued: 08/20/2019
• 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:
  US16/029,629
• Time in Patent Office: 408 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 data collection engine, a plurality of items including radio-frequency identification chips, a plurality of third party data and insight sources, a plurality of interfaces, client devices, a server and method thereof for preventing suicide. The server includes trained machine learning models, business logic and attributes of a plurality of patient events. The data collection engine sends attributes of new patient events to the server. The server can predict a suicide risk of the new patient events based upon the attributes of the new patient events utilizing the trained machine learning models. Using business logic, data visualization and the trained machine learning models, the server can also make recommendations to reduce the risk of suicides.

Claims

1. A method for predicting a suicide risk associated with a new event, the method comprising:

receiving a plurality of input attributes of the new event;

performing pre-processing on the plurality of input attributes to generate an input data set;

generating an output value from a trained model based upon the input data set; and

classifying the output value into a suicide risk category.

2. The method of claim 1, wherein the plurality of input attributes are data associated with an electronic health record.

3. The method of claim 1, wherein the plurality of input attributes includes a pain scale rating value.

4. The method of claim 1, wherein the plurality of input attributes includes a substance abuse history value.

5. The method of claim 1, wherein the plurality of input attributes includes a suicide attempt history.

6. The method of claim 1, wherein the plurality of input attributes includes an anger rating value.

7. The method of claim 1, wherein the plurality of input attributes includes an inpatient psychiatric hospitalization history.

8. The method of claim 1, wherein the plurality of input attributes includes a prior diagnosis of one or more of depression, panic, anxiety and bipolar disorder.

9. The method of claim 1, wherein the plurality of input attributes includes a family loss history.

10. The method of claim 1, wherein the plurality of input attributes includes a rating of patients desire to continue to live or die.

11. The method of claim 1, further comprising:

storing a plurality of past events, each of the plurality of past events including a plurality of patient attributes and a quantifiable outcome; and

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

performing pre-processing on the plurality of patient attributes for each of the plurality of past events to generate a plurality of input data sets;

dividing the plurality of past 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 method of claim 11, wherein the performing of the MLA includes measuring a global error in each training iteration for the NNM by:

calculating a local error, the local error being a difference between the output value of the NNM and the quantifiable outcome;

calculating the global error by summing all of the local errors in accordance with one of;

(1) Mean Square Error (MSE) formula

13. The method of claim 1, wherein the trained model is a trained Self-Organizing Map (SOM) including a plurality of network nodes arranged in a grid or lattice and in fixed topological positions, an input layer with a plurality of input nodes representing the input attributes of the past events, wherein each of the plurality of input nodes is connected to all of the plurality of network nodes by a plurality of synaptic weights.

14. The method of claim 1, further comprising:

storing a plurality of past events, each of the plurality of past events including a plurality of patient attributes and a quantifiable outcome;

performing pre-processing on the plurality of patient attributes for each of the plurality of past events to generate a plurality of input data sets; and

training a SOM to generate the trained model, wherein the training of the SOM includes;

initializing values of the plurality of synaptic weights to random values, randomly selecting one past event and determining which of the plurality of network nodes is a best matching unit (BMU) according to a discriminant function, wherein the discriminant function is a Euclidean Distance; and

iteratively calculating a neighborhood radius associated with the BMU to determine neighboring network nodes for updating, and updating values of synoptic weights for neighboring network nodes within the calculated neighborhood radius for a fixed number of iterations to generate the trained model.

15. The method of claim 14, further comprising generating another SOM including the plurality of patient attributes to reduce dimensionality.

16. The method of claim 1, wherein the classifying the output value into the suicide risk category further includes classifying the output value into one of a high risk category, moderate risk category, low risk category and minimal risk category, wherein the suicide risk category based upon a risk of suicide with a following 72 hours.