A system for telemedicine diagnostics through remote sensing includes a computing device configured to initiate a communication interface between the computing device and a client device operated by a human subject, wherein the secure communication interface includes an audiovisual streaming protocol, receive, from at least a remote sensor at the human subject, a plurality of current physiological data, generate a clinical measurement approximation as a function of the change of a first discrete and a second discrete set of current physiological data, wherein generating further comprises receiving approximation training data correlating physiological data with clinical measurement data, training a measurement approximation model as a function of the training data and a machine-learning process, and generating the clinical measurement approximation as a function of the current physiological data and the measurement approximation model, and presenting the clinical measurement approximation to a user of the computing device using the secure communication interface.

Embodiments for intelligent generation of customized questions or questionnaires by a processor. One or more customized questions or questionnaires may be generated according to a user profile, similar profiles of alternative users, one or more historical interactions with the alternative users, one or more goals defined by a domain experts, domain knowledge, historical questions or questionnaires, or a combination thereof using a machine learning operation.

Embodiments for intelligent generation of customized questions or questionnaires by a processor. One or more customized questions or questionnaires may be generated according to a user profile, similar profiles of alternative users, one or more historical interactions with the alternative users, one or more goals defined by a domain experts, domain knowledge, historical questions or questionnaires, or a combination thereof using a machine learning operation.

A system for smart pooling includes a computing device configured to obtain a feature datum, identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum, and determine an enhanced well count.

A computer-implemented method for providing anonymized patient datasets, comprises: analyzing statistical population data to ascertain obfuscation parameters; and anonymizing patient datasets including quasi-identifiers as attributes by obfuscating the quasi-identifiers of the patient datasets based on the obfuscation parameters to generate the anonymized patient datasets. A system includes at least one processor and a memory, and is configured to provide the anonymized patient datasets.

A virtual care system can include a location module configured to receive location information associated with a device from the device, and a graphical user interface (GUI) module configured to generate a medical provider user interface accessible via the device. The medical provider user interface can be contextually generated based on the location information and includes interface characteristics associated with the location information.

Embodiments of a risk assessment and intervention platform architecture are disclosed, where the risk assessment and intervention platform can predict and reduce negative medical outcomes. Embodiments of the risk assessment and intervention platform may include components with controls or interface elements to receive instructions from clinicians to set parameters for interventions and then to enroll, track, and monitor patient activity through the medical treatment plans. Embodiments of a scoring engine are configured based on a specific medical treatment plan and intervention protocols to initiate intervention as needed.

Disclosed herein is a hair health assessment, treatment, and styling diagnosis and recommendation method and system. The techniques use science and technology in order to aid users in achieving optimal health and beauty of their hair. The present disclosure allows a user to provide hair samples, such as digital images of hair, that can be analyzed using Machine Learning (ML) and Artificial Intelligence (AI) technology to provide recommendations including hair health assessments, hair care regimes, hair care products, and hair care services in an accurate, efficient, and automated manner. The present disclosure may employ a digital image capture device, either stand alone device or an embedded camera of a mobile device or a microscopic attachment, in order to capture digital images of their hair at a smartphone camera magnification level from a selfie image or microscopic-level of magnification, as a hair sample to be analyzed by the system.

Systems and methods for electronically creating and modifying a fitness plan are disclosed. The method may include receiving electronic user data, collecting electronic fitness data, and displaying a suggestion for a fitness activity based on the electronic user data and the electronic fitness data.

Systems and methods for electronically creating and modifying a fitness plan are disclosed. The method may include receiving electronic user data, collecting electronic fitness data, and displaying a suggestion for a fitness activity based on the electronic user data and the electronic fitness data.