The present disclosure provides a pharmaceutical composition for multi-stage release of psychoactive substances including cannabinoids. The pharmaceutical composition comprises two or more staged compositions having different release profiles or different release time such that the one or more active agents in each of the two or more staged compositions are released into the subject's blood stream at different time points.

Techniques for generating an ontology based on biomarker information associated with persons to facilitate improving clinical predictions relating to medical conditions are presented. An ontology generator component (OGC) can extract clinical features associated with patients and their associated times from medical records or databases to develop clinical profiles associated with the patients and relating to a medical condition. OGC can develop an ontology relating to the medical condition, including progression and severity of biomarkers associated with the medical condition, based on the clinical profiles and domain knowledge information relating to the medical condition. OGC can determine global features relating to progression and severity associated with the medical condition based on the ontology. At a forecasting point, the global features can be extracted from the ontology and applied to a prediction model to enhance prediction of onset of, or progression of, the medical condition for a patient.

Techniques for generating an ontology based on biomarker information associated with persons to facilitate improving clinical predictions relating to medical conditions are presented. An ontology generator component (OGC) can extract clinical features associated with patients and their associated times from medical records or databases to develop clinical profiles associated with the patients and relating to a medical condition. OGC can develop an ontology relating to the medical condition, including progression and severity of biomarkers associated with the medical condition, based on the clinical profiles and domain knowledge information relating to the medical condition. OGC can determine global features relating to progression and severity associated with the medical condition based on the ontology. At a forecasting point, the global features can be extracted from the ontology and applied to a prediction model to enhance prediction of onset of, or progression of, the medical condition for a patient.

A processor performing machine learning including spectral clustering can receive data from the sensor. Graph Laplacian of the data can be created and stored in a memory device. Spectral characteristic can be created by applying density of states and spectral gaps can be detected in an unsupervised manner in the spectral characteristic to determine r as number of clusters to cluster the data. A range space of a rational matrix of the graph Laplacian can be determined. K-means clustering can be performed on the range space of rational matrix of the graph Laplacian using r as the number of clusters, the K-means clustering returning r clusters of the received data.

A processor performing machine learning including spectral clustering can receive data from the sensor. Graph Laplacian of the data can be created and stored in a memory device. Spectral characteristic can be created by applying density of states and spectral gaps can be detected in an unsupervised manner in the spectral characteristic to determine r as number of clusters to cluster the data. A range space of a rational matrix of the graph Laplacian can be determined. K-means clustering can be performed on the range space of rational matrix of the graph Laplacian using r as the number of clusters, the K-means clustering returning r clusters of the received data.

A system detects a user's mood and in response establishes computer settings including computer game settings, recommends social network interactions, advises other users, alters task scheduling, and in general enhances collective group mood, collective productivity, social interaction, and engagement.

A simulation system performs microsimulations to model the impact of one or more early cancer detection screenings for a plurality of participants to simulate a randomized controlled trial (RCT). In one instance, the microsimulations are performed using parallel processing techniques. The microsimulation simulates the impact of early detection screenings on individual trajectories of the participants. In particular, while most screening modalities are for single cancer types, the microsimulation herein simulates the effect of a detection model on individual trajectories for participant populations having multiple types of cancer using, for example, multi-cancer early detection (MCED) screenings that are capable of detecting multiple types of cancer.

Systems and methods are provided for identifying a therapeutic VR activity or exercise for a subject/patient based on the subject's impairments, dynamically adjusting a VR activity for a patient, and identifying potential impairments based on a patient's performance in a VR activity. Patients may each have various physical, neurological, cognitive, and/or sensory impairments to be treated. Not all therapeutic activities may be appropriate for some patients and their impairments. A VR therapeutic activity platform may increase patient engagement and challenge patients at more appropriate times by better matching activities corresponding to a patient's impairments and dynamically adjusting each VR activity based on performance to offer a challenging and rewarding therapeutic experience.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

A method for presenting a patient frequent readmission recommendation, comprising: (i) receiving patient information comprising a plurality of demographic and/or medical features; (ii) extracting the features from the information; (iii) analyzing the features to determine whether the patient is a frequent readmission patient or is at risk of being a frequent readmission patient; (iv) estimating, if the patient is determined to be a frequent readmission patient, whether the frequent readmission is due to a medical condition and/or a socioeconomic condition, or predicting a frequent readmission risk level if the patient is determined to be at risk of being a frequent readmission patient; (v) generating a recommendation based at least in part on the estimated condition or the frequent readmission risk level, wherein the recommendation comprises a medical intervention and/or a socio-behavioral intervention; and (vi) providing (180) the recommendation via a user interface.