A method of identifying chronic pain in a patient including using functional magnetic resonance imaging (fMRI), performing a functional brain scan in the NAc (nucleus accumbens) of a patient brain including extracting activity from the NAc. Database information, which includes fMRI data obtained from healthy patients, may be compared to the extracted activity to determine if patient is a chronic pain patient. In patients with chronic pain, the method may be repeated to evaluate the effects of the treatment. A resting state brain scan may be performed initially, and a Fourier transform may be performed to obtain frequency content. The frequency bands of the method may be broken down to extract information in a 0.01-0.027 Hz frequency band.

A method of identifying chronic pain in a patient including using functional magnetic resonance imaging (fMRI), performing a functional brain scan in the NAc (nucleus accumbens) of a patient brain including extracting activity from the NAc. Database information, which includes fMRI data obtained from healthy patients, may be compared to the extracted activity to determine if patient is a chronic pain patient. In patients with chronic pain, the method may be repeated to evaluate the effects of the treatment. A resting state brain scan may be performed initially, and a Fourier transform may be performed to obtain frequency content. The frequency bands of the method may be broken down to extract information in a 0.01-0.027 Hz frequency band.

A data analysis system according to the present invention includes: a training data acquisition unit that acquires a combination of training data including information about a medicinal drug and a plurality of pieces of classification information for classifying the training data on the basis of a plurality of classification standards; a learning unit that learns a pattern of the information about the medicinal drug from distribution of data elements which constitute at least part of the training data and appear according to the classification information; an unknown data acquisition unit that acquires unknown data from a specified information source; a data evaluation unit that evaluates the acquired unknown data on the basis of the learned pattern with respect to each of the plurality of classification standards; and a presentation unit that presents the information about the medicinal drug included in the unknown data to a user according to evaluation by the data evaluation unit.

A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.

A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.

Systems and methods are disclosed for question generation to obtain more related medical information based on observed symptoms from a patient. In embodiments, possible diseases associated with the observed symptoms are generated by querying a knowledge graph. In embodiments, candidate symptoms associated with the possible diseases are also identified and are combined with the observed symptoms to obtain combined symptom sets. In embodiments, discriminative scores for the candidate symptom sets are determined and candidate symptoms with top discriminative scores are selected. In embodiments, these selected candidate symptoms may be checked for conflicts with observed symptoms and removed from further consideration if a conflict exists. In embodiments, one or more questions may be generated based on the remaining selected candidate systems to aid in collecting information about the patient. In embodiments, the process may be repeated with the updated observed symptoms.

Disclosed is a system and method for predictive risk assessment and intervention including a risk assessment unit that receives survey data from a remotely connected survey device. The survey data comprises information about the social and cultural environment of one or more members of a risk population, including the member's perceptions of their social and environmental factors, the member's demographic data, and optionally publicly available data associated with the member's geographic environment. A predictive risk assessment unit analyzes perceived risk hierarchy inventories to generate a risk portfolio for each surveyed member of the population, which risk portfolio may include a risk predictive quotient profile for each such member assigning a numeric value indicating a likelihood of that member engaging in certain negative activities, a recommendation of interventions that are determined to reduce the likelihood of such member engaging in those negative activities, and preferably a record of success and/or failure of various interventions in reducing that risk. Intervention partners then administer the interventions to the surveyed members, record the success or failure of such intervention in preventing the identified dangerous behavior, and transmit an intervention effectiveness report to the predictive risk assessment unit. The predictive risk assessment unit may then modify and recalibrate the survey instrument and the associated recommended intervention products and tools to maximize the successes of interventions.

Disclosed is a system and method for predictive risk assessment and intervention including a risk assessment unit that receives survey data from a remotely connected survey device. The survey data comprises information about the social and cultural environment of one or more members of a risk population, including the member's perceptions of their social and environmental factors, the member's demographic data, and optionally publicly available data associated with the member's geographic environment. A predictive risk assessment unit analyzes perceived risk hierarchy inventories to generate a risk portfolio for each surveyed member of the population, which risk portfolio may include a risk predictive quotient profile for each such member assigning a numeric value indicating a likelihood of that member engaging in certain negative activities, a recommendation of interventions that are determined to reduce the likelihood of such member engaging in those negative activities, and preferably a record of success and/or failure of various interventions in reducing that risk. Intervention partners then administer the interventions to the surveyed members, record the success or failure of such intervention in preventing the identified dangerous behavior, and transmit an intervention effectiveness report to the predictive risk assessment unit. The predictive risk assessment unit may then modify and recalibrate the survey instrument and the associated recommended intervention products and tools to maximize the successes of interventions.

Systems and methods for determining diagnostic and/or prognostic risk of having or developing brain injury related symptoms after a head impact includes a point-of-care assay reader, a PHI smart device application, and a neurocognitive/vestibular smart device application. The diagnosis and prognosis server application includes instructions stored on a non-transitory computer-readable medium executed on the server that receives patient protected health information (PHI) from the PHI smart device application, receives neurocognitive test results from a neurocognitive testing application, and receives assay results from the point-of-care assay reader and generates a diagnostic score and a prognostic risk scores for post-acute traumatic brain injury TBI symptom categories as measures of patient outcomes.

Systems and methods for determining diagnostic and/or prognostic risk of having or developing brain injury related symptoms after a head impact includes a point-of-care assay reader, a PHI smart device application, and a neurocognitive/vestibular smart device application. The diagnosis and prognosis server application includes instructions stored on a non-transitory computer-readable medium executed on the server that receives patient protected health information (PHI) from the PHI smart device application, receives neurocognitive test results from a neurocognitive testing application, and receives assay results from the point-of-care assay reader and generates a diagnostic score and a prognostic risk scores for post-acute traumatic brain injury TBI symptom categories as measures of patient outcomes.