Described herein are computer-implemented methods for analyzing an input image of a mouth region from a user to provide information regarding a disease or condition of the mouth region, a computing device configured to receive the input images from a user; and a trained machine learning system. In some embodiments, the computing device is configured to transmit an oral health score to the user.

A method and device for predicting Alzheimer's disease based on voice characteristics are provided. The device for predicting Alzheimer's disease according to an embodiment includes: a voice input unit configured to generate a voice sample by recording a voice of a subject; a data input unit configured to receive demographic information of the subject; a voice characteristic extraction unit configured to extract voice characteristics from the generated voice sample; and a prediction model that is pre-trained to predict presence or absence of Alzheimer's disease in the subject, based on the voice characteristics and the demographic information.

An apparatus for providing information about a user's brain resources is provided. The apparatus includes at least sensor interface circuitry and processing circuitry coupled to the sensor interface circuitry. In a calibration mode, the sensor interface circuitry is configured to receive first sensor data from an electroencephalography sensor. The first sensor data are indicative of an electroencephalogram of the user. Further, the sensor interface circuitry is configured to receive second sensor data from a physiological sensor in the calibration mode. The second sensor data are indicative of a physiological property of the user. In the calibration mode, the processing circuitry is configured to train a brain-physiological model for the user based on the first sensor data and the second sensor data.

An apparatus for providing information about a user's brain resources is provided. The apparatus includes at least sensor interface circuitry and processing circuitry coupled to the sensor interface circuitry. In a calibration mode, the sensor interface circuitry is configured to receive first sensor data from an electroencephalography sensor. The first sensor data are indicative of an electroencephalogram of the user. Further, the sensor interface circuitry is configured to receive second sensor data from a physiological sensor in the calibration mode. The second sensor data are indicative of a physiological property of the user. In the calibration mode, the processing circuitry is configured to train a brain-physiological model for the user based on the first sensor data and the second sensor data.

A method is provided for treatment of atherosclerotic cardiovascular disease in a subject in need thereof by administering a statin pharmaceutical composition to the subject when the subject is qualified for over-the-counter access to the statin pharmaceutical composition. In some embodiments, the statin pharmaceutical composition includes atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, or simvastatin as an active ingredient.

An apparatus (10) for recommending medical tests for a patient includes at least one electronic processor (20) programmed to retrieve patient data stored in a database (32); compare the patient data with guidelines (34) for a plurality of medical tests and identify at least one recommended medical test of the plurality of medical tests based on the comparison; and output, on at least one display device (24), a recommendation (40) for the patient to undergo the at least one recommended medical test.

A patient care system may collect and analyze data regarding characteristics of an individual to identify any health, medical, and/or physiological conditions associated with the individual. Trend analysis may predict an onset of a medical condition associated with the individual. Trend analysis may determine and/or identify one or more target areas (e.g., geographical areas) where individuals experience particular symptoms and/or affected by various health, medical, and/or mental issues. An alert, diagnosis, and/or treatment plan may be determined for an individual based on a corpus of information collected from and/or associated with multiple individuals.

A patient care system may collect and analyze data regarding characteristics of an individual to identify any health, medical, and/or physiological conditions associated with the individual. Trend analysis may predict an onset of a medical condition associated with the individual. Trend analysis may determine and/or identify one or more target areas (e.g., geographical areas) where individuals experience particular symptoms and/or affected by various health, medical, and/or mental issues. An alert, diagnosis, and/or treatment plan may be determined for an individual based on a corpus of information collected from and/or associated with multiple individuals.

A system and method is provided for training operators (e.g., users), including new nurses or nephrologists as well as patients or their family members, to learn the intricacies of dialysis (e.g., hemodialysis) treatment through a simulation. The simulation may afford users the ability to fail catastrophically in simulated dialysis treatment, see the consequences, learn and internalize complex algorithms, and develop the ability to think in a time pressured situation. The simulation may thereby provide a safe learning environment for experiencing decisions and consequences of these decisions when performing a dialysis treatment. In some instances, the simulation may be in a mobile application form factor to encourage more ubiquitous use on personal devices, which may provide ease of access for the dialysis training. The simulation may further include time-based simulated scenarios that increase in difficulty and complexity between levels.

A method for displaying content items to a user based on selected goals from the user includes performing an initial assessment of a user, which includes retrieving at least one of measured patient disease management data and user-inputted patient disease management data from a user database, requesting additional information from the user for determining one or more goals, and receiving the additional information from the user via a user interface. The method includes recommending a plurality of goals based on the initial assessment and stored protocols related to disease management retrieved from a content database, receiving a selection of a goal from the user, receiving goal tracking information indicative of progress toward the selected goal, selecting one or more content items from the content database based on at least one of the selected goal and the goal tracking information, and displaying the selected one or more content items.