A system is provided that furnishes expert procedural guidance based upon patient-specific data gained from surgical instruments incorporating sensors on the instrument's working surface, one or more reference sensors placed about the patient, sensors implanted before, during or after the procedure, the patient's personal medical history, and patient status monitoring equipment. Embodiments include a system having a surgical instrument with a sensor for generating a signal indicative of a property of a subject tissue of the patient, which signal is converted into a current dataset and stored. A processor compares the current dataset with other previously stored datasets, and uses the comparison to assess a physical condition of the subject tissue and/or to guide a procedure being performed on the tissue.

An apparatus for generating a prediction that a patient will experience a seizure by monitoring a patient's body temperature over time is provided. The apparatus may include a sensor to sense temperature. The apparatus may monitor, using the sensor, the body temperature of the patient and compare the body temperature over a first period of time and a second period of time. The apparatus may generate a prediction of whether the patient will experience a seizure following the second period of time based at least in part on a result of the comparing.

A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.

Systems and methods for health and body simulations in order to predict numerous physiological parameters in a subject or a population of subjects based on the input of limited physiological data.

Systems and methods are disclosed to detect a potential syncope event using cardiac acceleration information of a patient and to transition a medical device from a first low-power mode to a second high-power mode in response to the detected potential syncope event.

The present subject matter discloses a system(s) and a method(s) for determining a health state of an individual. According to an embodiment, a method comprises measuring, by a heart rate sensor, a heart rate of the individual during operation within the environment. The method further comprises outputting, by a pressure sensing platform, pressure data of the individual. Further, the method comprises outputting, by an image capturing device, image data of the individual. The method further comprises inferring, by a processing unit, an amount of fat of the individual in the image data. The method further comprises updating, by the processing unit, the amount of fat of the individual using the pressure data. The method further comprises controlling, by the processing unit, a threshold for determining the health state of the individual, using the amount of fat and the heart rate of the individual.

A blood pressure prediction method and an electronic device using the same are provided. The method includes the following steps. A training data set is collected. A first blood pressure prediction model is established according to the training data set. Hemodialysis parameter data of a target patient is received, wherein the hemodialysis parameter data includes a first hemodialysis parameter at a previous time point and a second hemodialysis parameter at a current time point. A hemodialysis parameter variation amount between the first hemodialysis parameter and the second hemodialysis parameter is calculated. The hemodialysis parameter variation amount is provided to the first blood pressure prediction model to generate a prediction blood pressure variation associated with a next time point. An operation is performed according to the prediction blood pressure variation of the target patient.

A vehicle includes a processing device that detects an authentication device approaching the vehicle, determines a user's impaired state based on user condition information acquired using at least one device mounted on the vehicle, and performs a safe driving assistance service. The safe driving assistance service can prevent an impaired user from operating the vehicle.

Dysglycemia as a risk factor for neurodevelopmental disorder or developmental diabetes. The risk is assessed based on measurement of a glucose control indicator in a blood sample. One particular example of a neurodevelopmental disorder is retinopathy of prematurity in an infant. One particular example of a glucose control indicator is ‘comprehensive glycated hemoglobin fraction’ or ‘comprehensive glycated albumin fraction.’ This is calculated using ‘total whole blood protein’ in the denominator. In the case of chronic hyperglycemia, there is an increased risk of proliferative retinopathy of prematurity. In the case of chronic hypoglycemia, there is an increased risk of non-proliferative retinopathy of prematurity. This ‘total whole blood protein’ technique could also be used to determine the glucose control status in other types of patients.

A bed-leaving prediction device (server device) (10) is connected through a digital communication network (60) to: a portable information processing terminal (40) of care staff; environmental sensors (32 to 34) for detecting environment values such as temperature in a room; a human sensor (31); and a bed sensor (35). A bed-leaving prediction processing section (115) calculates a bed-leaving prediction value indicative of a degree of possibility that a care recipient leaves a sleeping furniture after a second time interval has expired since a current time point based on a plurality of environment values detected in a time period between the current time point and a time point before expiration of a first time interval, outputs of the human sensor, and outputs of the bed sensor. A bed-leaving notification processing section (117) compares the bed-leaving prediction value with a threshold value, and transmits, to the portable information processing terminal, a bed-leaving notification indicating that the care recipient leaves the sleeping furniture after the second time interval expires when the bed-leaving prediction value exceeds the threshold value.