# US 2021/0343418 A1 · Community resource network oneview > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0343418 A1 - Document ID: US20210343418A1 - Status: Published application - Kind: A1 - Application number: 17/244,138 - Filed: 2021-04-29 - Published: 2021-11-04 - Earliest claimed: 2020-04-30 - Assignees: QS Systems, Inc. - Applicants: QS Systems, Inc. - Inventors: Richard Arzt Thompson; Jeffrey Michael Greene; Lalo Abelardo Valdez; Salim Kizaraly; Jason McRoy - Web page: https://patentgrep.com/patent/US20210343418A1 ## CPC - G16H 40/67: for remote operation - G16H 50/30: for calculating health indices; for individual health risk assessment - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Determining risks based on data from a plurality of disassociated domains can comprise obtaining data regarding an individual from each of the plurality of disassociated domains. The data regarding the individual can have format and content specific to the domain from which it is obtained. The obtained data can be tagged based on the domain from which it is obtained and a set of predefined elements for each of the plurality of disassociated domains. The tagged data can be associated with one or more of a plurality of predefined groups. Each group can represent one or more of the plurality of disassociated domains. A diagnostic process identifying a risk for the individual in each of the domains can be performed on the tagged data and a user interface including a visual representation of the identified risk in each domain in each of the predefined groups can be provided. ## Claims ### Claim 1 A method for determining risks based on data from a plurality of disassociated domains, the method comprising: obtaining, by a processor of a Social Information Exchange (SIE) platform, data regarding an individual from one or more systems of each of the plurality of disassociated domains, the data regarding the individual having format and content specific to the domain from which it is obtained; tagging, by the processor of the SIE platform, the obtained data based on the domain from which it is obtained and a set of predefined elements for each of the plurality of disassociated domains; associating, by the processor of the SIE platform, the tagged data with one or more of a plurality of predefined groups, each group representing one or more of the plurality of disassociated domains; performing, by the processor of the SIE platform, a diagnostic process on the tagged data, wherein the diagnostic process identifies a risk for the individual in each of the plurality of domains based on the tagged data; presenting, by the processor of the SIE platform, a user interface including a visual representation of the identified risk for the individual in each of the plurality of domains in each of the plurality of predefined groups. ### Claim 2 (depends on 1) The method of claim 1, wherein the plurality of predefined groups, the plurality of disassociated domains, and the set of predefined elements for each of the plurality of disassociated domains are based on a predefined set of Social Determinants of Health (SDoH). ### Claim 3 (depends on 1) The method of claim 1, further comprising storing, by the processor of the SIE platform, the tagged data in the format specific to the domain from which it is obtained. ### Claim 4 (depends on 3) The method of claim 3, further comprising executing, by the processor of the SIE platform, one or more queries on the tagged data stored in the format specific to the domain from which it is obtained. ### Claim 5 (depends on 3) The method of claim 3, further comprising normalizing, by the processor of the SIE platform, the tagged data. ### Claim 6 (depends on 5) The method of claim 5, wherein performing the diagnostic process on the tagged data is performed on the normalized tagged data and comprises: scoring each element of the set of predefined elements for each of the plurality of disassociated domains; generating an aggregated score for each domain of the plurality of disassociated domains based on the scored elements in each domain, wherein the identified risk for the individual in each of the plurality of domains in each of the plurality of predefined groups in the visual representation of the user interface is based on the scoring of each element of the set of predefined elements for each of the plurality of disassociated domains. ### Claim 7 (depends on 1) The method of claim 1, further comprising: updating, by the processor of the SIE platform, the risk for the individual in each of the plurality of domains based obtaining new data regarding the individual from at least one of the plurality of disassociated domains, tagging the obtained new data, associating the tagged new data with one or more of the plurality of predefined groups, normalizing the tagged new data, and performing the diagnostic process on the normalized new data; and applying, by the processor of the SIE platform, a machine learning process based on the updated risk, wherein the machine learning process updates at least one element of the set of predefined elements for at least one of the plurality of disassociated domains. ### Claim 8 A system comprising: a processor; and a memory coupled with and readable by the processor and storing therein a set of instructions which, when executed by the processor, causes the processor to determine risks based on data from a plurality of disassociated domains by: obtaining data regarding an individual from one or more systems of each of the plurality of disassociated domains, the data regarding the individual having format and content specific to the domain from which it is obtained; tagging the obtained data based on the domain from which it is obtained and a set of predefined elements for each of the plurality of disassociated domains; associating the tagged data with one or more of a plurality of predefined groups, each group representing one or more of the plurality of disassociated domains; performing a diagnostic process on the tagged data, wherein the diagnostic process identifies a risk for the individual in each of the plurality of domains based on the tagged data; presenting a user interface including a visual representation of the identified risk for the individual in each of the plurality of domains in each of the plurality of predefined groups. ### Claim 9 (depends on 8) The system of claim 8, wherein the plurality of predefined groups, the plurality of disassociated domains, and the set of predefined elements for each of the plurality of disassociated domains are based on a predefined set of Social Determinants of Health (SDoH). ### Claim 10 (depends on 8) The system of claim 8, wherein the instructions further cause the processor to store the tagged data in the format specific to the domain from which it is obtained. ### Claim 11 (depends on 10) The system of claim 10, wherein the instructions further cause the processor to execute one or more queries on the tagged data stored in the format specific to the domain from which it is obtained. ### Claim 12 (depends on 10) The system of claim 10, wherein the instructions further cause the processor to normalize the tagged data. ### Claim 13 (depends on 12) The system of claim 12, wherein performing the diagnostic process on the tagged data is performed on the normalized tagged data and comprises: scoring each element of the set of predefined elements for each of the plurality of disassociated domains; generating an aggregated score for each domain of the plurality of disassociated domains based on the scored elements in each domain, wherein the identified risk for the individual in each of the plurality of domains in each of the plurality of predefined groups in the visual representation of the user interface is based on the scoring of each element of the set of predefined elements for each of the plurality of disassociated domains. ### Claim 14 (depends on 8) The system of claim 8, wherein the instructions further cause the processor to: update the risk for the individual in each of the plurality of domains based obtaining new data regarding the individual from at least one of the plurality of disassociated domains, tagging the obtained new data, associating the tagged new data with one or more of the plurality of predefined groups, normalizing the tagged new data, and performing the diagnostic process on the normalized new data; and apply a machine learning process based on the updated risk, wherein the machine learning process updates at least one element of the set of predefined elements for at least one of the plurality of disassociated domains. ### Claim 15 A non-transitory, computer-readable medium comprising a set of instructions stored therein which, when executed by a processor, causes the processor to determine risks based on data from a plurality of disassociated domains by: obtaining data regarding an individual from one or more systems of each of the plurality of disassociated domains, the data regarding the individual having format and content specific to the domain from which it is obtained; tagging the obtained data based on the domain from which it is obtained and a set of predefined elements for each of the plurality of disassociated domains; associating the tagged data with one or more of a plurality of predefined groups, each group representing one or more of the plurality of disassociated domains; performing a diagnostic process on the tagged data, wherein the diagnostic process identifies a risk for the individual in each of the plurality of domains based on the tagged data; presenting a user interface including a visual representation of the identified risk for the individual in each of the plurality of domains in each of the plurality of predefined groups. ### Claim 16 (depends on 15) The non-transitory, computer-readable medium of claim 15, wherein the plurality of predefined groups, the plurality of disassociated domains, and the set of predefined elements for each of the plurality of disassociated domains are based on a predefined set of Social Determinants of Health (SDoH). ### Claim 17 (depends on 15) The non-transitory, computer-readable medium of claim 15, wherein the instructions further cause the processor to store the tagged data in the format specific to the domain from which it is obtained and execute one or more queries on the tagged data stored in the format specific to the domain from which it is obtained. ### Claim 18 (depends on 17) The non-transitory, computer-readable medium of claim 17, wherein the instructions further cause the processor to normalize the tagged data. ### Claim 19 (depends on 18) The non-transitory, computer-readable medium of claim 18, wherein performing the diagnostic process on the tagged data is performed on the normalized tagged data and comprises: scoring each element of the set of predefined elements for each of the plurality of disassociated domains; generating an aggregated score for each domain of the plurality of disassociated domains based on the scored elements in each domain, wherein the identified risk for the individual in each of the plurality of domains in each of the plurality of predefined groups in the visual representation of the user interface is based on the scoring of each element of the set of predefined elements for each of the plurality of disassociated domains. ### Claim 20 (depends on 15) The non-transitory, computer-readable medium of claim 15, wherein the instructions further cause the processor to: update the risk for the individual in each of the plurality of domains based obtaining new data regarding the individual from at least one of the plurality of disassociated domains, tagging the obtained new data, associating the tagged new data with one or more of the plurality of predefined groups, normalizing the tagged new data, and performing the diagnostic process on the normalized new data; and apply a machine learning process based on the updated risk, wherein the machine learning process updates at least one element of the set of predefined elements for at least one of the plurality of disassociated domains.