Abstract: Techniques regarding autonomously determining an entity's susceptibility towards one or more treatment services are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an assessment component that can determine a susceptibility disposition value that measures a susceptibility of an entity to a treatment service based on a trust disposition value. The trust disposition value can be determined based on a communication with the entity using machine learning technology.

Abstract: A method, a structure, and a computer system for assessing user mobility. The exemplary embodiments may include collecting mobility data corresponding to a user and extracting one or more low level features from the mobility data. The exemplary embodiments may further include extracting one or more clinical level features from the low level features and assessing one or more health conditions of the user based on applying one or more models to the one or more clinical level features.

Abstract: This document describes techniques for, and systems that enable, in-ear health monitoring. The techniques described herein enable early detection of health conditions (e.g., contagious disease) through use of an in-ear health-monitoring and audio device. These techniques prompt a user, often through the user's smart phone, to listen to audio content through the device, which also takes the user's temperature. Through repetitive use, the techniques are capable of determining a temperature differential for the user, which aids in early detection of a contagious disease or other malady.

Abstract: A sleeping mask includes a signal processor for processing sensor data, an infrared light source coupled to the signal processor and configured to emit infrared light toward an eyelid of a user, and an array of infrared sensors coupled to the signal processor and configured to receive infrared light reflected from the eyelid of the user.

Abstract: Techniques regarding autonomously determining an entity's susceptibility towards one or more treatment services are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an assessment component that can determine a susceptibility disposition value that measures a susceptibility of an entity to a treatment service based on a trust disposition value. The trust disposition value can be determined based on a communication with the entity using machine learning technology.

Abstract: Techniques regarding pain treatment are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include: a data collection component that can determine at least one parameter associated with a pain perception of a subject, a computing component that can determine a relationship between the pain perception and the at least one parameter using artificial intelligence, and can determine a treatment for the subject based on the relationship; and a treatment component that can cause a device associated with the subject to apply at least a portion of the treatment.

Abstract: Techniques regarding pain treatment are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include: a data collection component that can determine at least one parameter associated with a pain perception of a subject, a computing component that can determine a relationship between the pain perception and the at least one parameter using artificial intelligence, and can determine a treatment for the subject based on the relationship; and a treatment component that can cause a device associated with the subject to apply at least a portion of the treatment.

Abstract: Techniques regarding autonomously updating the status of a prescription are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a prescription component that can update a status of a prescription associated with an entity based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected effectiveness of the prescription.

Abstract: Techniques regarding autonomously updating the status of a medical procedure are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a procedure component that can update a status of a medical procedure regarding an entity based on a trust disposition value derived using machine learning technology. The trust disposition value can be indicative of an expected efficacy associated with the medical procedure.

Abstract: Techniques regarding autonomously updating a participation status of an entity with regards to a clinical trial are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise status component that can update a participation status of an entity regarding a clinical trial based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected enrichment contribution associated with the participation status.

Abstract: Techniques regarding autonomously updating the status of a medical procedure are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a procedure component that can update a status of a medical procedure regarding an entity based on a trust disposition value derived using machine learning technology. The trust disposition value can be indicative of an expected efficacy associated with the medical procedure.

Abstract: Techniques regarding autonomously updating the status of a prescription are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a prescription component that can update a status of a prescription associated with an entity based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected effectiveness of the prescription.

Abstract: Techniques regarding autonomously updating a participation status of an entity with regards to a clinical trial are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise status component that can update a participation status of an entity regarding a clinical trial based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected enrichment contribution associated with the participation status.

Abstract: Techniques regarding autonomously updating a participation status of an entity with regards to a clinical trial are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise status component that can update a participation status of an entity regarding a clinical trial based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected enrichment contribution associated with the participation status.

Abstract: Techniques regarding autonomously updating the status of a medical procedure are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a procedure component that can update a status of a medical procedure regarding an entity based on a trust disposition value derived using machine learning technology. The trust disposition value can be indicative of an expected efficacy associated with the medical procedure.

Abstract: Techniques regarding autonomously controlling the delivery of one or more chemical compounds are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a compound component can identify a chemical compound mixture to be distributed to an entity based on a trust disposition value. The trust disposition value can be determined using machine learning technology and is indicative of an expected effectiveness of the chemical compound mixture with regards to the entity.

Abstract: Techniques regarding autonomously determining an entity's susceptibility towards one or more treatment services are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an assessment component that can determine a susceptibility disposition value that measures a susceptibility of an entity to a treatment service based on a trust disposition value. The trust disposition value can be determined based on a communication with the entity using machine learning technology.

Abstract: Techniques regarding pain treatment are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include: a data collection component that can determine at least one parameter associated with a pain perception of a subject, a computing component that can determine a relationship between the pain perception and the at least one parameter using artificial intelligence, and can determine a treatment for the subject based on the relationship; and a treatment component that can cause a device associated with the subject to apply at least a portion of the treatment.

Abstract: Techniques regarding autonomously updating the status of a prescription are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a prescription component that can update a status of a prescription associated with an entity based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected effectiveness of the prescription.

Abstract: This document describes automated nursing assessments. Automation of the nursing assessment involves a nursing-assessment device that makes determinations of a person's mood, physical state, psychosocial state, and neurological state. To determine a mood and physical state of a person, video of the person is captured while the person is positioned in front of an everyday object, such as a mirror. The captured video is then processed according to human condition recognition techniques, which produces indications of the person's mood and physical state, such as whether the person is happy, sad, healthy, sick, vital signs, and so on. In addition to mood and physical state, the person's psychosocial and neurological state are also determined. To do so, questions are asked of the person. These questions are determined from a plurality of psychosocial and neurological state assessment questions, which include queries regarding how the person feels, what the person has been doing, and so on.