# US 2016/0259901 A1 · Determining disease state of a patient by mapping a topological module representing the disease, and using a weighted average of node data

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- Number: US 2016/0259901 A1
- Document ID: US20160259901A1
- Status: Published application
- Kind: A1
- Application number: 15/144,859
- Filed: 2016-05-03
- Published: 2016-09-08
- Earliest claimed: 2012-05-25
- Assignees: Renew Group Private Limited
- Applicants: Renew Group Private Limited
- Inventors: James B. SEWARD
- Web page: https://patentgrep.com/patent/US20160259901A1

## CPC

- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G16H 40/63: for local operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- G01N 2800/00: Detection or diagnosis of diseases
- G16H 50/50: for simulation or modelling of medical disorders
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)

## Abstract

A medical general intelligence computer system and computer-implemented methods analyze morpho-physiological numbers for determining a risk of an emergent disease state, determining an emergent disease state, predicting a pre-emergent disease state, determining a pre-emergent disease state, and/or predicting a risk of a pre-emergent disease state.

## Claims

### Claim 1

A computer-implemented method for determining a disease state of a patient, the method comprising:
a processor accessing from a computer-readable non-transitory memory, computer-readable data representing a general graph having nodes of physiological conditions, wherein each of the physiological conditions is represented by each of the nodes, wherein each of the nodes is connected by one or more edges representing correlations between the nodes;
the processor accessing from the computer-readable non-transitory memory, computer-readable data of physiological conditions of a patient;
the processor executing computer-readable instructions for quantification of the physiological conditions of the patient;
the processor transforming the physiological conditions of the patient to node data by performing quantification of the physiological conditions of the patient;
the processor executing computer-readable instructions for associating the node data to the general graph;
the processor creating an individualized graph by associating the node data to the general graph;
the processor storing the individualized graph to the computer-readable non-transitory memory;
the processor accessing from the computer-readable non-transitory memory, computer-readable data representing a topological module of nodes connected by edges, wherein the topological module represents the disease state;
the processor executing computer-readable instructions for mapping the topological module representing the disease state to the individualized graph;
the processor executing computer-readable instructions for determining a weight averaged value of the node data of the topological module of nodes;
the processor determining the disease state of the patient from the weight averaged value;
the processor executing computer-readable instructions for creating a new general graph by replacing all of the nodes inside the topological module in the general graph with a super node of the weight averaged value; and
the processor storing the new general graph to the computer-readable non-transitory memory.

### Claim 2 (depends on 1)

The computer-implemented method as in claim 1, further comprising:
the processor accessing from the computer-readable non-transitory memory, additional computer-readable data representing another topological module which includes the super node, wherein the topological module represents another disease state;
the processor executing computer-readable instructions for mapping the another topological module representing the another disease state to the individualized graph; and
the processor executing computer-readable instructions for determining another weight averaged value of the super node and other data of the another topological module.

### Claim 3 (depends on 2)

The computer-implemented method as in claim 2, further comprising:
the processor mapping the another topological module to the individualized graph, wherein the another disease state is an emergent disease state.

### Claim 4 (depends on 2)

The computer-implemented method as in claim 2, further comprising:
the processor mapping the another topological module to the individualized graph, wherein the another disease state is a pre-emergent disease state.

### Claim 5

A computer-implemented method for determining a second disease state of a patient, the method comprising:
a processor accessing from a computer-readable non-transitory memory, computer-readable data representing a general graph having nodes of physiological conditions and at least one super node representative of a first disease state, wherein each of the physiological conditions is represented by each of the nodes, wherein each of the nodes and the at least one super node is connected by one or more edges representing correlations between the nodes and at least one super node;
the processor accessing from the computer-readable non-transitory memory, computer-readable data of physiological conditions of a patient;
the processor executing computer-readable instructions for quantification of the physiological conditions of the patient;
the processor transforming the physiological conditions of the patient to node data by performing quantification of the physiological conditions of the patient;
the processor executing computer-readable instructions for associating the node data to the general graph;
the processor creating an individualized graph by associating the node data to the general graph;
the processor storing the individualized graph to the computer-readable non-transitory memory;
the processor accessing from the computer-readable non-transitory memory, computer-readable data representing a topological module of nodes and at least one super node connected by edges, wherein the topological module represents the second disease state;
the processor executing computer-readable instructions for mapping the topological module representing the second disease state to the individualized graph;
the processor executing computer-readable instructions for determining a weight averaged value of the node data and the at least one super node of the topological module; and
the processor determining the second disease state of the patient from the weight averaged value.

### Claim 6

A medical general intelligence computer, comprising:
a processor; and
a computer-readable non-transitory memory in communication with the processor, the computer-readable non-transitory memory including processor-executable instructions for determining a pre-emergent disease state of a patient,
wherein when the processor executes the processor-executable instructions,
the processor storing on the computer-readable non-transitory memory, computer-readable data representing a general graph of physiological conditions and at least one disease state, wherein each of the physiological conditions is represented as a node of the general graph, and the at least one disease state is represented as a super node of the general graph,
wherein each of the nodes and the super node is connected by one or more edges representing correlations between the nodes and the super node;
the processor storing on the computer-readable non-transitory memory, computer-readable data representing a topological module of the nodes and the super node connected by edges, wherein the topological module represents the second disease state, and the topological module can be mapped onto a portion of the general graph; and
the processor storing on the computer-readable non-transitory memory, a weight averaged value of nodes and the super node of the portion of the general graph that has been mapped onto by the topological module for determining a second disease state.