Abstract: Technologies are provided for determining an individual's likelihood of relapsing into prior behavior subsequent to a treatment for a mental health or addiction disorder, and in some instances predicting a likelihood time frame for such a relapse. Target subjects having a risk of addiction relapse, non-adherence to a treatment program, or absconding, may be automatically identified based on a multiplicative-regression model for relative survival (MRS) that is developed for predicting risk or likelihood of relapse or non-adherence. Further, in some embodiments, a leading indicator of near-term future abnormalities may be provided thereby proactively notifying supervisory personnel responsible for the person and providing such personnel with timely notice to enable effective corrective, preventive, or trend-modifying maneuvers to be undertaken.

Abstract: A diagnostic process for disease detection using gene expression based PCA (Principal Component Analysis) classifier is provided. The present invention includes a method of diagnosing disease in a patient by performing a multilayer PCA classification that analyzes gene expression profiles of patients' biological samples to predict their class as disease or healthy, uses patients' biological samples to extract a set of fingerprint genes for specific disease and cell type which can be used as identification features, and classifies patient biological samples as disease or healthy based on differential gene expression profiles of the fingerprint genes.

Abstract: A system including a range of motion, quality of sleep, overall, and control modules. The range of motion module, prior to a procedure being performed on a patient, determines a first range of motion score of the patient based on a first signal generated by a sensor. The quality of sleep module, prior to the procedure being performed on the patient, determines a first quality of sleep score or a first pain score based on the first signal. The overall module determines a combined score based on the first range of motion score and the first quality of sleep score or the first pain score. The control module compares the combined score to a predetermined threshold and predicts an outcome of the procedure based on the comparison. The control module, based on the combined score, determines whether to perform the procedure, adjust the procedure or refrain from performing the procedure.

Abstract: In a method for advanced identification of a customer, a customer may remotely place an order, intending to later go to a store to pick up the ordered item. The store may have a pick-up area (e.g., at the back of the store) where the customer can go to pick up the ordered item. To save the customer time, the customer may be identified when she enters the store so that an employee can obtain her ordered item and have it ready to pick up by the time the customer walks through the store and arrives at the pick-up area.

Abstract: Individual-specific changes in health conditions are detected using a latent space mapping generated from baseline physiological data collected in a longitudinal study of the individual. Historical baseline data in an n-dimensional input space is modeled into a k-dimensional latent space, where k<n. The mapping for the model is then used to convert new physiological data into a latent space point, which can be used to determine if the associated newly collected physiological data is anomalous and thus indicative of a change in health condition. Anomalies can be inferred from poor fit between the point and baseline data within the latent space, or form large error between data reconstructed into the input space from the point and the original new physiological data.

Abstract: Techniques are disclosed for determining a risk assessment corresponding to a level of risk that a user of a service organization has or will develop a condition. The techniques include receiving user data associated with a user record of a user. The techniques further include inputting the user data into a trained prediction model of a user condition prediction system. The user data may include a plurality of data points, weighted relative to each other, that respectively correspond to one of a plurality of categories. The techniques further include determining, by the trained prediction model, a risk assessment of a user condition based on the user data. The risk assessment may then be provided to a user device of a user service agent for use coordinating user service.

Abstract: An example embodiment may involve (i) receiving, by a server device, deoxyribonucleic acid (DNA) information associated with a user; (ii) parsing, by the server device, the DNA information to identify one or more single nucleotide polymorphisms (SNPs); (iii) determining, by the server device and based on the identified SNPs, an endocannabinoid genotype of the user; (iv) determining, based on the endocannabinoid genotype of the user, a recommendation of one or more cannabinoid formulations; (v) transmitting, to a client device associated with the user, a web-based representation of a first graphical user interface; and (vi) receiving, from the client device, an indication to display a detailed representation of a particular cannabinoid formulation of the one or more cannabinoid formulations.

Abstract: The present disclosure includes an inventory fulfillment system for managing information regarding pertaining to the inventory supply chain logistics and automation of medical items dispensed in conjunction with medical treatment of a patient at a medical facility, wherein the medical items are stored in a physical inventory space in the medical facility, wherein the medical items have radiofrequency tags attached thereto is disclosed herein. In some implementations, the inventory fulfillment system receives, dispenses, and manages, medical items that are provided by a multiplicity of suppliers, collects patients' payments. In some implementations, the inventory fulfillment system also includes assigning items to patients, updating inventory applications, insurance eligibility, patient estimation, and patient payment.

Abstract: Systems, methods and computer-readable media are provided for determining conformity to performance of meaningful use measures in human health care delivery. A Bayesian Markov Chain Monte Carlo statistical process is utilized to achieve reliable estimates for such measures despite the small subgroup sample sizes accruing during each measurement period. One embodiment utilizes zero- and one-inflated beta regression that is robust against moderate prevalence of zero or one counts in the numerators for such measurements and determinations of statistical associations with such factors as clinician, care venue, and patient attributes. Based on the determined conformity, a notification is provided to provider clinicians or organization management indicating the conformity and, in some instances, a degree of conformity.

Abstract: Systems and methods for tracking biological samples are presented. In some embodiments, intrinsic and extrinsic biological sample data and/or intrinsic and extrinsic patient data are used to label a biological sample obtained at a point-of-care facility. The labeled biological sample may be sent to a processing facility, wherein the labeled biological sample undergoes analysis and/or processing into a labeled therapeutic. The labeled therapeutic is delivered back to the point-of-care facility, wherein the labeled therapeutic is tested for the presence of the additives prior to administration to a patient. Multi-factor validation is performed, including validating the identity of the patient to a container, validating the identity of the labeled biological sample/therapeutic to the container, and thus, validating the identity of the labeled therapeutic to the patient.

Abstract: A system can include a plurality of infusion pumps and a connectivity adapter in a clinical environment. The connectivity adapter can receive update data, such as a drug library update or an operational software update, and can store the update data within the clinical environment. The connectivity adapter can send the update data to a predetermined number of infusion pumps that have requested the update. At least two subsets of the infusion pumps can receive different blocks of the update data at about the same time. Further, the same or different update data can be provided to the infusion pumps at about the same time.

Abstract: Disclosed is an system for predicting end-stage renal disease complication risk based on contrastive learning, including an end-stage renal disease data preparation module, configured to extract structured data of a patient by using a hospital electronic information system and daily monitoring equipment, and process the structured data to obtain augmented structured data; and a complication risk prediction module, configured to construct a complication representation learning model and a complication risk prediction model, perform training and learning on the augmented structured data through the complication representation learning model to obtain a complication representation, and perform end-stage renal disease complication risk prediction by using the complication representation through the complication risk prediction model.

Abstract: Systems and methods are provided for analyzing blood of a living being, and for presenting guidance for medical treatment.

Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with one of the principal components, selecting features of the training set most closely correlated with the principal components, performing a regression analysis on the selected features to determine a subset of the selected features that are most closely correlated with a model target, training a machine learning model with the subset, verifying the trained machine learning model with a verification set, and saving the verified trained machine learning model as the intervention model. The method includes determining an intervention expectation indicating a likelihood that the user will take action in response to an intervention being execute, determining a likelihood of a gap in care for the user, selecting and scheduling an intervention for execution based on the care gap likelihood and the intervention expectation.

Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with a principal component, selecting features of the training set most highly correlated with principal components, training a machine learning model with at least some of the selected features, and saving the verified trained machine learning model as the intervention model. The method includes determining multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the user will take action in response to an intervention being executed using the engagement channel corresponding to the channel-specific intervention expectation. The method includes selecting an intervention and scheduling the selected intervention for execution.

Abstract: An embodiment includes displaying to a physician at least one of: (a)(i) a map of a hospital floor, (a)(ii) a first patient identifier, and (a)(iii) a second patient identifier, wherein (b)(i) the map displays first and second rooms of the hospital floor, (b)(ii) a first patient is assigned to the first room and the first patient identifier, (b)(iii) a second patient is assigned to the second room and a second patient identifier, and (b)(iv) a first nurse is assigned to the first room and the first patient identifier, (b)(v) and a second nurse is assigned to the second room and the second patient identifier; receiving a selection from the physician, the selection comprising at least one of the displayed first room and the displayed first patient identifier; and sending a communication from the physician to the first nurse, and not the second nurse, in response to receiving the selection.

Abstract: A medication administration and verification system includes scanning a code for a cavity of a multi-dose package to identify at least one previously prescribed medication determined at a time of packaging the at least one previously prescribed medication in the cavity. A list is received in response to the electronically encoded package identifier for at least one currently prescribed medication. At least one visual indicator is displayed respectively corresponding to the at least one currently prescribed medication for the patient for administration during the medication administration event, and a presence or exclusion of each of the currently prescribed medication in the cavity is automatically confirmed for the presence or the exclusion of in the cavity.

Abstract: Given information identifying a user who is not logged into a system, the user is provided a guest order status of one or more pharmacy orders; the guest order status masks sensitive information of the user.

Abstract: Provided are computer implemented method and systems for providing and monitoring patient compliance with a patient healthcare treatment plan. The method includes receiving, from a healthcare provider over a network, application features for generating a patient application including patient instructions for using a medical therapy, and generating an application for a patient. The application includes at least an input for the user to input data for use in evaluating patient compliance with a treatment plan. In addition, the method includes receiving, from the healthcare provider over the network, a prescription for the application for the patient, and activating the application after the patient receives training on use of the application. The method also may include receiving patient compliance data from the application over the network based on the input.

Abstract: Systems and methods are provided for receiving a data set that includes demographic data and population data; training, based on the data set, a neural network to establish a relationship between different physical layouts of messages and responses to the different physical layouts of the messages; applying the trained neural network to a user profile to predict a physical layout of a message; generating instructions for an electronic device based on the predicted physical layout of the message, the instructions comprising the message; and transmitting the instructions to the electronic device to create a physical label having a layout corresponding to the predicted physical layout of the message.