Signal processing for making predictive determinations

1. A system comprising:

a server system communicably couplable via a computer network with a plurality of sending systems comprising electronic devices that generate messages to be transmitted via the computer network, the server system comprising:

memory configured to store computer-executable instructions; and

a processor configured to access the memory to execute the computer-executable instructions to collectively at least provide a predictive condition engine communicatively coupled with a routing engine;

the server system configured to access messages in a data stream prior to delivery of the messages to one or more destination devices for storage, the messages originating from the plurality of sending systems in the computer network and transmitted to one or more destination devices for storage, where the one or more destination devices are distinct from the server system;

the predictive condition engine of the server system to:

responsive to user input via a particular electronic device of the electronic devices corresponding to generation of one or more messages, identify a particular message from the messages accessed prior to delivery to the one or more destination devices, the particular message comprising a note, where the note comprises observation data corresponding to a dependent user, the observation data comprising unstructured data including indicia of one or more conditions corresponding to the dependent user, the unstructured data comprising sensed data collected from an area proximate to the dependent user;

evaluate the unstructured data in the note using a natural language processing model to identify subjective indicators present in the note at least in part by mapping of identified words in the note to a selected outcome from a set of outcomes and providing a structured output corresponding the mapping in a structured format, the subjective indicators indicating at least a sentiment of an authorized user with respect to the dependent user that the predictive condition engine identify as supporting the selected outcome, where the selected outcome specifies a cellular abnormality, the cellular abnormality being a cellular condition potentially present in the dependent user;

assign a weight value to each of the identified subjective indicators based on a respective correspondence of the identified subjective indicators to the cellular abnormality at least partially by evaluating the structured output of against a learning model that specifies relevance of the identified subjective indicators to the cellular abnormality and to other subjective indicators;

obtaining a set of rules that define thresholds of condition scores assigned to each condition specified by a corresponding outcome of the set of outcomes to determine a composite abnormality score based on the weight values of the identified subjective indicators and relationships between the identified subjective indicators, the composite abnormality score corresponding to a presence of the cellular abnormality in the dependent user;

determine whether the cellular abnormality is present in the dependent user based on a first comparison of the composite abnormality score with a normality threshold; and

generate a cellular abnormality report that identifies at least the presence of the cellular abnormality and the dependent user;

the routing engine of the server system to:

assign a response time boundary to the cellular abnormality report based on a criticality specification of an assessment corresponding to the cellular abnormality report; and

cause transmission of the cellular abnormality report and the assigned response time boundary to an electronic device and/or user interface mapped to at least one recipient of a set of one or more recipients.

2. The system of claim 1 , wherein the processor is further configured to access the memory to execute the computer-executable instructions to collectively at least:

determine, based on the weight values of the identified subjective indicators and the relationships between the identified subjective indicators, a plurality of type scores corresponding to a plurality of cellular abnormality types to which the cellular abnormality potentially belongs; and

determine, based on a second comparison of the plurality of type scores with respective type thresholds, that the cellular abnormality present in the dependent user is a particular type of cellular abnormality of the plurality of cellular abnormality types.

3. The system of claim 2 , wherein the cellular abnormality report further identifies the particular type of cellular abnormality.

4. The system of claim 1 , wherein the processor is further configured to access the memory to execute the computer-executable instructions to collectively at least:

add the cellular abnormality report to a validation queue; and

allow the authorized user or a different authorized user to access the cellular abnormality report from the validation queue.

5. The system of claim 1 , wherein the processor is further configured to access the memory to execute the computer-executable instructions to collectively at least provide the cellular abnormality report to a user device accessible by the authorized user.

6. The system of claim 5 wherein:

the cellular abnormality report includes at least a portion of the note; and

the processor is further configured to access the memory to execute the computer-executable instructions to collectively at least, prior to providing the cellular abnormality report to the user device, graphically highlight at least a portion of the identified subjective indicators present in the portion of the note.

7. The system of claim 1 , wherein the subjective indicators comprise words, terms, and/or phrases that are indicative of one or more concepts associated with cellular abnormalities.

8. A computer-implemented method comprising:

accessing, by a computer system communicably couplable via a computer network with a plurality of sending systems comprising electronic devices that generate messages to be transmitted via the computer network toward a plurality of different destination devices, messages in a data stream prior to delivery of the messages to one or more destination devices for storage, the messages originating from the plurality of sending systems in the computer network;

responsive to user input via a particular electronic device of electronic devices corresponding to generation of one or more messages, identifying, using a predictive condition engine of the computer system, a particular message from the messages accessed prior to delivery to the one or more destination devices, the particular message that comprises a note, where the note comprises observation data corresponding to a dependent user, the observation data comprising unstructured data including indicia of one or more conditions corresponding to the dependent user, the unstructured data comprising sensed data collected from an area proximate to the dependent user;

evaluating, using the predictive condition engine, the unstructured data in the note using a natural language processing model to identify subjective indicators present in the note at least in part by mapping of identified words in the note to a selected outcome from a set of outcomes and providing a structured output corresponding the mapping in a structured format, the subjective indicators indicating at least a sentiment of an authorized user with respect to the dependent user that the predictive condition engine identify as supporting the selected outcome, where the selected outcome specifies a cellular abnormality, the cellular abnormality being a cellular condition potentially present in the dependent user;

assigning, using the predictive condition engine, a weight value to each of the identified subjective indicators based on a respective correspondence of the identified subjective indicators to the cellular abnormality at least partially by evaluating the structured output of against a learning model that specifies relevance of the identified subjective indicators to the cellular abnormality and to other subjective indicators;

obtaining a set of rules that define thresholds of condition scores assigned to each condition specified by a corresponding outcome of the set of outcomes to determining, using the predictive condition engine, a composite abnormality score based on the weight values of the identified subjective indicators and relationships between the identified subjective indicators, the composite abnormality score corresponding to a presence of the cellular abnormality in the dependent user;

determining, using the predictive condition engine, whether the cellular abnormality is present in the dependent user based on a first comparison of the composite abnormality score with a normality threshold; and

generating, using the predictive condition engine, a cellular abnormality report that identifies at least the presence of the cellular abnormality and the dependent user; and

utilizing a routing engine of the computer system to:

assign a response time boundary to the cellular abnormality report based on a criticality specification of an assessment corresponding to the cellular abnormality report; and

cause transmission of the cellular abnormality report and the assigned response time boundary to an electronic device and/or user interface mapped to at least one recipient of a set of one or more recipients.

9. The computer-implemented method of claim 8 , further comprising:

determining, based on the weight values of the identified subjective indicators and the relationships between the identified subjective indicators, a plurality of type scores corresponding to a plurality of cellular abnormality types to which the cellular abnormality potentially belongs; and

determining, based on a second comparison of the plurality of type scores with respective type thresholds, that the cellular abnormality present in the dependent user is a particular type of cellular abnormality of the plurality of cellular abnormality types.

10. The computer-implemented method of claim 9 , wherein the cellular abnormality report further identifies the particular type of cellular abnormality.

11. The computer-implemented method of claim 8 , further comprising:

adding the cellular abnormality report to a validation queue; and

allowing the authorized user or a different authorized user to access the cellular abnormality report from the validation queue.

12. The computer-implemented method of claim 8 , further comprising providing the cellular abnormality report to a user device accessible by the authorized user.

13. The computer-implemented method of claim 12 , wherein:

the cellular abnormality report includes at least a portion of the note; and

the method further comprises, prior to providing the cellular abnormality report to the user device, graphically highlighting at least a portion of the identified subjective indicators present in the portion of the note.

14. The computer-implemented method of claim 8 , wherein the subjective indicators comprise words, terms, and/or phrases that are indicative of one or more concepts associated with cellular abnormalities.

15. The computer-implemented method of claim 8 , wherein the note is a report that describes causes and effects associated with cellular abnormalities.

16. One or more non-transitory, computer-readable storage media comprising computer-executable instructions that, when executed by one or more computer systems, cause the one or more computer systems to perform operations comprising:

accessing, using a predictive condition engine of the one or more computer systems, messages in a data stream prior to delivery of the messages to one or more destination devices for storage, the messages originating from a plurality of sending systems in a computer network and transmitted to one or more destination devices for storage, where the one or more destination devices are distinct from the one or more computer systems;

responsive to user input via a particular electronic device of electronic devices corresponding to generation of one or more messages, identifying, using the predictive condition engine, a particular message from the messages accessed prior to delivery to the one or more destination devices, the particular message comprising a note, where the note comprises observation data corresponding to a dependent user, the observation data comprising unstructured data including indicia of one or more conditions corresponding to the dependent user, the unstructured data comprising sensed data collected from an area proximate to the dependent user;

evaluating, using the predictive condition engine, the unstructured data in the note using a natural language processing model to identify subjective indicators present in the note at least in part by mapping of identified words in the note to a selected outcome from a set of outcomes and providing a structured output corresponding the mapping in a structured format, the subjective indicators indicating at least a sentiment of an authorized user with respect to the dependent user that the predictive condition engine identify as supporting the selected outcome, where the selected outcome specifies a cellular abnormality, the cellular abnormality being a cellular condition potentially present in the dependent user;

assigning, using the predictive condition engine, a weight value to each of the identified subjective indicators based on a respective correspondence of the identified subjective indicators to the cellular abnormality at least partially by evaluating the structured output of against a learning model that specifies relevance of the identified subjective indicators to the cellular abnormality and to other subjective indicators;

obtaining a set of rules that define thresholds of condition scores assigned to each condition specified by a corresponding outcome of the set of outcomes to determine, using the predictive condition engine, composite abnormality score based on the weight values of the identified subjective indicators and relationships between the identified subjective indicators, the composite abnormality score corresponding to a presence of the cellular abnormality in the dependent user;

determining, using the predictive condition engine, whether the cellular abnormality is present in the dependent user based on a first comparison of the composite abnormality score with a normality threshold; and

generating, using the predictive condition engine, a cellular abnormality report that identifies at least the presence of the cellular abnormality and the dependent user;

utilizing a routing engine of the one or more computer systems to assign a response time boundary to the cellular abnormality report based on a criticality specification of an assessment corresponding to the cellular abnormality report; and

causing transmission, using the routing engine, of the cellular abnormality report and the assigned response time boundary to an electronic device and/or user interface mapped to at least one recipient of a set of one or more recipients.

17. The one or more non-transitory, computer-readable storage media of claim 16 , wherein the operations further comprise:

determining, based on the weight values of the identified subjective indicators and the relationships between the identified subjective indicators, a plurality of type scores corresponding to a plurality of cellular abnormality types to which the cellular abnormality potentially belongs; and

determining, based on a second comparison of the plurality of type scores with respective type thresholds, that the cellular abnormality present in the dependent user is a particular type of cellular abnormality of the plurality of cellular abnormality types.

18. The one or more non-transitory, computer-readable storage media of claim 17 , wherein the cellular abnormality report further identifies the particular type of cellular abnormality.

19. The one or more non-transitory, computer-readable storage media of claim 16 , wherein the operations further comprise providing the cellular abnormality report to a user device accessible by the authorized user or accessible by a different authorized user.

20. The one or more non-transitory, computer-readable storage media of claim 16 , wherein the note is a report that describes causes and effects associated with cellular abnormalities.