Abstract: A system and a method directed to various techniques for creating an autonomous multipurpose application using a platform of application programming interfaces (APIs). In one embodiment, a method comprising operations performed by a cognitive intelligence platform is disclosed. The method includes: publishing, for implementation in the autonomous multipurpose application, at least one API that uses artificial intelligence to perform analytics based on a knowledge graph pertaining to ontological data of a medical condition, wherein the at least one API is included in a set of APIs; providing the at least one API in a user interface of a computing device; receiving, from the computing device, a request to subscribe to the at least one API; implementing computer instructions of the at least one API in the autonomous multipurpose application; and providing the autonomous multipurpose application to the computing device for execution on the computing device.

Abstract: A method for operating an administrator viewer on an administrator computing device. The method includes receiving first information pertaining to a service performed by a person for a user. The method further includes receiving an action instruction generated by an artificial intelligence engine of a cognitive intelligence platform based on a data structure associated with a user and a condition of the user, wherein the action instruction specifies that an administrator performs an action tailored for the user based on the first information. The method further includes presenting the first information and the action instruction in a screen of the administrator viewer.

Abstract: A method for operating a patient viewer on a computing device of a patient is described herein. The method may include receiving an action instruction for the patient to perform, wherein the action instruction pertains to a condition of the patient, and the action instruction is generated based on the condition of the patient by an artificial intelligence engine of a cognitive intelligence platform. The method may further include presenting the action instruction in a first screen of the patient viewer, receiving medical records comprising information about the condition of the patient, and presenting at least a portion of the medical records in a second screen of the patient viewer. The method may further include receiving recommended curated content pertaining to the condition of the patient to educate the patient about the condition and presenting the recommended curated content in a third screen of the patient viewer.

Abstract: A method may include receiving a set of codes pertaining to an event performed for a patient, mapping the plurality of codes to a taxonomy of data to determine a utilization unit, mapping the utilization unit to ontological data of a medical condition, mapping the ontological data to a knowledge fragment pertaining to the medical condition and the patient, and causing the knowledge fragment to be presented on a computing device of a medical personnel.

Abstract: A method for operating a clinic viewer on a computing device of a medical personnel is disclosed. The method includes receiving a reason that a patient scheduled an appointment with the medical personnel. The method further includes receiving a condition diagnosed for the patient and a care plan generated for the condition, wherein the care plan is generated by an artificial intelligence engine of a cognitive intelligence platform. The method further includes presenting, on the clinic viewer, the care plan and a watch-list comprising the reason, the condition, or some combination thereof.

Abstract: A method for controlling distribution of information pertaining to a medical condition is disclosed. The method may include receiving, at a server, an electronic medical record including notes pertaining to a patient. The method may also include processing the notes to obtain indicia. The method may also include identifying a possible medical condition of the patient by identifying a similarity between the indicia and a knowledge graph representing knowledge pertaining to the possible medical condition, wherein the knowledge graph includes a set of nodes representing the information pertaining to the possible medical condition. The method may also include providing, at a first time, first information of the information to a computing device of the patient for presentation on the computing device, the first information being associated with a root node of the set of nodes.

Abstract: A method is disclosed for managing risk associated with a medical condition diagnosed for a plurality of patients in a population, the method including creating, using an artificial-intelligence engine of a cognitive intelligence platform, a population profile including a plurality of patient graphs associated with the medical condition and the plurality of patients in the population, wherein each of the plurality of patient graphs includes information pertaining to how engaged a respective patient is with managing the medical condition. The method further including determining, based on the population profile, the risk associated with the medical condition, wherein the risk includes a potential inadequacy in management of the medical condition. The method may also include performing an intervention based on the risk.

Abstract: A method and system for an autonomous multipurpose application for scheduling, check-in, and education is described herein. The autonomous multipurpose application may provide an improved experience for users that schedule appointments with people having specialties, and that check-in to the scheduled appointments. A cognitive intelligence platform may use artificial intelligence techniques to enable operations of the autonomous multipurpose application. Further, computing resources may be reduced using the described techniques.

Abstract: A method includes receiving, at an artificial intelligence engine, a corpus of data for a patient, where the corpus of data includes a set of strings of characters. The method also includes identifying, in the set of strings of characters, indicia including a phrase, a predicate, a keyword, a subject, an object, a cardinal, a number, a concept, or some combination thereof. The method also includes comparing the indicia to a knowledge graph representing known health related information to generate a possible health related information pertaining to the patient. The method also includes identifying, using a logical structure, a structural similarity of the possible health related information and a known predicate in the logical structure. The method also includes generating, by the artificial intelligence engine, cognified data based on the structural similarity.

Abstract: A method for diagnosing a medical condition through cognification of unstructured data is disclosed. The method may include receiving, at a server, an electronic medical record including notes pertaining to a patient. The method may also include generating cognified data using the notes, where the cognified data includes a health summary of the medical condition. The method may also include generating, based on the cognified data, a diagnosis of the medical condition of the patient, where the diagnosis at least identifies a type of the medical condition. The method may also include providing the diagnosis to a computing device for presentation on the computing device.

Abstract: A method for maintaining content pertaining to healthcare in a hyperledger, the method including receiving, from a computing device associated with a medical personnel entity, a transaction request to perform an operation on the hyperledger, wherein the operation includes storing content pertaining to healthcare in the hyperledger. The method also including executing one or more rules of the hyperledger to determine whether to allow the operation to be performed, wherein at least one of the one or more rules includes determining whether the medical personnel entity is associated with an authorizing credential pertaining to healthcare. Responsive to determining that the one or more rules of the hyperledger are satisfied, the method also including performing the operation on the hyperledger to store the content in the hyperledger.

Abstract: A method for performing a medical transaction using a hyperledger, the method including receiving, from a computing device associated with a medical personnel entity, a request to perform the medical transaction between the medical personnel entity and a patient entity, where the medical personnel entity is associated with a medical personnel node and the patient entity is associated with a patient node, and a respective copy of the hyperledger is maintained at both the medical personnel node and the patient node. The method includes identifying a transaction in the hyperledger that stores an authorizing credential of the medical personnel entity, determining whether to allow the medical transaction to be performed based on at least the authorizing credential of the medical personnel entity, and responsive to determining to allow the medical transaction to be performed, updating the respective copy of the hyperledger with the medical transaction.

Abstract: A method for answering questions posed by a user, the method comprising: receiving from a medical conversational user interface a user-generated natural language medical information query at an artificial intelligence-based medical conversation cognitive agent; extracting a medical question; compiling a medical conversation language sample; extracting internal medical concepts and medical data from the sample; inferring a therapeutic intent of the user; generating a therapeutic paradigm logical framework, wherein logical framework comprises medical logical progression paths from the medical question to respective therapeutic answers, each of the logical progression paths includes medical logical linkages from the medical question to a therapeutic path-specific answer, and the medical logical linkages include the internal medical concepts and external therapeutic paradigm concepts derived from a store of medical subject matter ontology data; selecting a likely medical information path based upon the therapeuti

Abstract: A computer-implemented method for answering natural language medical information questions posed by a user of a medical conversational interface of a cognitive artificial intelligence system, the method comprising: receiving from a medical conversational user interface a user-generated natural language medical information query at an artificial intelligence-based medical conversation cognitive agent; extracting a medical question from the query; compiling a medical conversation language sample; extracting internal medical concepts and medical data from the sample, where the internal medical concepts include descriptions of medical attributes of the medical data entities; inferring a therapeutic intent of the user; generating a therapeutic paradigm logical framework for interpreting of the medical question, wherein logical framework comprises a catalog of medical logical progression paths from the medical question to respective therapeutic answers, each of the logical progression paths includes one or more med

Abstract: A cognitive intelligence platform including: a first system configured to execute a knowledge cloud, the first system including: a first memory storing instructions that cause the knowledge cloud to: receive inputs from medical facilities; and receive inputs from service providers; a second system configured to implement a critical thinking engine, the second system including: a second memory storing instructions that cause the critical thinking engine to receive inputs from the knowledge cloud; and a third system configured to implement a cognitive agent, the third system including: a third memory storing instructions that cause the cognitive agent to: receive an originating questions from a user related to a subject matter; execute, using the critical thinking engine, a first round of analysis to generate an answer; and provide the answer to the user including a recommendation associated with the subject matter.

Abstract: A method including receiving segments of a medical information natural language conversation stream at an artificial intelligence-based health information conversation agent from a user interface; responsive to medical information content of a user profile associated with the conversation stream, defining a desired clinical management outcome objective relevant to health management criteria and related health management data attributes of the user profile; identifying potential therapeutic interventions correlated to advancement of the clinical management outcome objective; selecting a medical intervention from the potential therapeutic interventions correlated to advancement of the outcome objective; presenting in the conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the medical intervention likely to advance the outcome objective; and presenting to the user in the stream a therapeutic advice conversation stream segment explaining a correlation between th

Abstract: A method for answering a user-generated natural language medical information query based on a diagnostic conversational template, including: receiving a medical information query at an artificial intelligence-based diagnostic conversation agent; responsive to content of the query, selecting a diagnostic fact variable set relevant to generating an answer for the query by classifying the query into a domain-directed medical query classification associated with respective diagnostic fact variable sets; compiling user-specific medical fact variable values for respective medical fact variables of the diagnostic fact variable set, where the compiling further includes: extracting a first set of user-specific medical fact variable values from a local user medical information profile associated with the query and requesting a second set of user-specific medical fact variable values through questions; and generating an answer in response to the query.

Abstract: A computer-implemented method for answering natural language medical information questions posed by a user of a medical conversational interface of a cognitive artificial intelligence system, the method comprising: receiving from a medical conversational user interface a user-generated natural language medical information query at an artificial intelligence-based medical conversation cognitive agent; extracting a medical question from the query; compiling a medical conversation language sample; extracting internal medical concepts and medical data from the sample, where the internal medical concepts include descriptions of medical attributes of the medical data entities; inferring a therapeutic intent of the user; generating a therapeutic paradigm logical framework for interpreting of the medical question, wherein logical framework comprises a catalog of medical logical progression paths from the medical question to respective therapeutic answers, each of the logical progression paths includes one or more med

Abstract: A method, non-transitory computer readable medium and apparatus for estimating a pulse transmit time (PTT) to calculate a blood pressure are disclosed. For example, the method includes receiving a series of video images over a period of time that includes a first location of an individual and a second location of the individual, calculating a first set of luminance values of the first location and a second set of luminance values of the second location, estimating the PTT based on an average time difference from consecutive peaks of the first set of luminance values and the second set of luminance values and transmitting the PTT to a blood pressure calculation device to calculate the blood pressure based on the PTT that is estimated.

Abstract: A system, method, and apparatus for determining risk associated with a plurality of loan accounts, having an off-line mode and an online mode. In the off-line mode a first plurality of account histories is received. A maximum value variable m is set. A definition is received of a predetermined maximum look-ahead timeframe p. An iterative variable i is set equal to zero. While i is less than the maximum value variable m, a plurality of variables associated with an account history equaling the iterative variable i are stored and i incremented by 1. A predictive multi-output risk model is trained. In the online mode, a second plurality of account histories is received. A determination is made which accounts have a future risk level greater than a current risk level, and a further determination made which accounts currently require one or more tasks. Accounts requiring tasks are automatically assigned.