A water prescribing system 1 is provided with a state storage unit 21 which stores a state variable relating to a physical constitution and health condition for each of a plurality of persons including a user, and a prescription unit 22 which creates a prescription of water suitable for the user based on the state variable.

Embodiments discussed herein facilitate generation of a prognosis for recurrence or non-recurrence of atrial fibrillation (AF) after pulmonary vein isolation (PVI). A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prognosis for AF after PVI based on radiographic images, alone or in combination with clinical features. A second set of embodiments discussed herein relates to determination of a prognosis for a patient for AF after PVI based on radiographic images, alone or in combination with clinical features.

Disclosed is an application which is stored in a computer-readable medium and executes an operation of a terminal device. An operation performed in the present application comprises the steps of: providing a UI screen for receiving an input of symptom information; when a user's symptom information is input through the UI screen, transmitting the symptom information and a waiting number request to a hospital management server; receiving a waiting number corresponding to the waiting number request from the hospital management server, and providing the UI screen including the received waiting number; transmitting the user's identification information to an application server; and controlling the application server to transmit the user's identification information to the hospital management server.

A computer-implemented method for determining a predictive disease status of an inflammatory disease of a patient is compatible with a system including a remote platform and at least one mobile user device, wherein the at least one mobile user device is associated with the patient and is in data communication with the platform. The method comprises: receiving, at the platform, monitoring data indicative of the health state of the patient from the user device associated to the patient; determining, at the platform, a predictive disease state of the inflammatory disease of the patient based on the received monitoring data; evaluating the determined predictive disease status; providing the predictive disease status to a user at the platform and/or the patient at the user device based on the evaluating the determined predictive disease status.

A computer-implemented method for determining a predictive disease status of an inflammatory disease of a patient is compatible with a system including a remote platform and at least one mobile user device, wherein the at least one mobile user device is associated with the patient and is in data communication with the platform. The method comprises: receiving, at the platform, monitoring data indicative of the health state of the patient from the user device associated to the patient; determining, at the platform, a predictive disease state of the inflammatory disease of the patient based on the received monitoring data; evaluating the determined predictive disease status; providing the predictive disease status to a user at the platform and/or the patient at the user device based on the evaluating the determined predictive disease status.

A method of training an artificial intelligence model for estimating a hemoglobin A1c (HbA1c) level includes collecting patient information including exercise information and bioinformation of a patient, collecting an actual HbA1c level of the patient, converting the collected patient information and actual HbA1c level into a single standardized data structure format, and training an artificial intelligence model using the converted patient information and actual HbA1c level to generate an artificial intelligence model for estimating an HbA1c level. The bioinformation includes at least one of a blood sugar level, a blood pressure, a heart rate, and a menstrual cycle, and the exercise information is generated on the basis of patient life log data acquired by a patient terminal.

Provided herein are methods and systems for making patient-specific therapy recommendations of a combination of any two or more therapies selected from a lipid-lowering therapy, an anti-inflammatory therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

The present disclosure relates to a method for determining a user-specific hair treatment with determination and inclusion of the degree of damage of the hair. To this end, the content of cysteic acid is firstly determined with the aid of near-infrared and/or infrared spectra of the keratin fibres of an individual and a degree of damage is derived via a calibration model. Individual treatment advice is output on the basis of the determined degree of damage.

The present disclosure relates to a method for determining a user-specific hair treatment with determination and inclusion of the degree of damage of the hair. To this end, the content of cysteic acid is firstly determined with the aid of near-infrared and/or infrared spectra of the keratin fibres of an individual and a degree of damage is derived via a calibration model. Individual treatment advice is output on the basis of the determined degree of damage.

In some examples, a computer system may receive medical records for a plurality of patients. The system may generate a plurality of data representations based on the medical records for the individual patients, each data representation representing at least a therapy performed and a quantified disability status of the individual patient. The system may select a subset of the data representations for which an improvement in the quantified disability status exceeds a threshold. Further, the system may receive patient information for a first patient and may generate a first data representation from the patient information. The system may compare the first data representation with the data representations in the subset to determine at least one therapy predicted to provide an improvement in the current quantified disability of the first patient. The system may send information related to the at least one therapy to a computing device.