Abstract: The system brings together patient data both clinical records and imaging studies from disparate sources to the user workstation or mobile device in real-time and on-demand. In order to do so, the system needs to establish application layer connectivity utilizing HL7 or FHIR and DICOM for imaging. Once a secure connection is established, the system is able to search and retrieve records and present it to end user.

Abstract: Techniques for obtaining materials science properties of soft tissue for use in a damage model for ameliorating injuries in an individual performing a process are presented. The techniques can include obtaining physical parameters characterizing the soft tissue of the individual under each of a plurality of loading conditions, fitting a soft tissue damage model based on the parameters, and ameliorating injury in performing the process by implementing guidelines based on the soft tissue damage model.

Abstract: The present application provides a personal supplement supply system that includes: a delivery device, which further includes a housing having a plurality of cartridges removably attached to a base located within the housing. Each of the plurality of cartridges store therein at least one dispensable ingredient. The device includes a processor coupled to computer memory, the computer memory having software stored thereon that when executed causes the delivery device to dispense the at least one dispensable ingredient from each of the plurality of cartridges in accordance with a bio-informed plan personalized for a user of the delivery device.

Abstract: Cohort definition and selection system for a computer having a memory, a central processing unit and a display, the system including: a cohort definition module to configure the memory according to a phenotype vector. The phenotype vector includes a patient ID to uniquely associate the phenotype vector to a patient, a plurality of demographic dimension fields, each demographic dimension field to describe a respective demographic aspect of the patient, a calculated dimension field to describe a calculated information related to the patient, a plurality of phenotype-based dimension fields, each phenotype-based dimension field to indicate relevance of the respective phenotype-based dimension field to the patient, and a child phenotype vector to recursively define a phenotype-based dimension field, and a cohort selection module to select a set of phenotype vectors that are within a predetermined error from a cohort selection criteria.

Abstract: The present invention proposes an apparatus and method for assisting a user to plan a puncture trajectory within a region-of-interest of a subject. The apparatus comprises: a data interface (211) configured to receive anatomical data of the region-of-interest of the subject and to output the at least one candidate puncture trajectory, and a data processor (213) for calculating at least one candidate puncture trajectory on basis of the anatomical data, multiple criteria, and a tradeoff among the multiple criteria. The anatomical data of the subject comprises three-dimensional data of the region of interest. The data processor (213) is further configured, upon receiving a first user input for adjusting the tradeoff among the multiple criteria, to adjust the tradeoff among the multiple criteria on basis of the first user input, and to calculate the at least one candidate puncture trajectory on basis of the anatomical data, the multiple criteria and the adjusted tradeoff among the multiple criteria.

Abstract: A system, method, and computer platform product for an adaptive cognitive training platform. In accordance with various aspects of the present disclosure, an adaptive cognitive training platform is configured to process user activity data from one or more instances of a computerized cognitive training program to determine a baseline cognitive assessment for one or more cognitive abilities/skills of a user. The cognitive assessment model may be configured to further process the user activity data to predict a relative value or measure of efficacy for one or more computerized stimuli or interactions within the computerized cognitive training program. The adaptive cognitive training platform may be configured to configure, modify and/or present one or more graphical user interface elements for one or more subsequent instances of the computerized cognitive training program according to the predicted value or measure of efficacy of one or more cognitive training tasks.

Abstract: Systems and techniques for monitoring, predicting and/or alerting for census periods in medical inpatient units are presented. A system can include a grouping component that defines a group of beds at a medical facility based on at least one grouping factor, and a group stability component that determines a measure of occupancy variability for the group based on historical census data for respective beds in the group. The system can further include a model selection component that selects one or more census forecasting models for the group based on the measure of occupancy variability, and a patient census component that applies the one or more census forecasting models to current patient flow data for the medical facility to forecast an expected occupancy level for the group during one or more future periods of time.

Abstract: A method for generating synthetic training records for use in training a model to predict low-incidence events. A synthetic training record is generated from a minority-class training record by substituting a different value for a feature in the minority-class training record, where the probability of the different value occurring in the minority-class training record exceeds a probability threshold. Also disclosed are a non-transitory storage medium comprising minority-class training records and synthetic training records and a method of training a machine-leaning model using training records augmented with synthetic training records. An exemplary synthetic training records is a synthetic medical record for use in training a model to predict drug overdoses.

Abstract: Management of a diabetic condition may be performed by detecting a mealtime and/or an increased level of activity associated with a user. The mealtime is detected based one or more mealtime indicators that may indicate a likelihood of an occurrence of a mealtime. Each mealtime indicator provides additional context around the likelihood of a mealtime. To assist in managing the diabetic condition, a user's elevated activity levels may be detected. The activity level of a user is detected based on one or more activity indicators. Each activity indicator provides additional context around the likelihood of an increased level of activity by the user. Management of the diabetic condition may be performed proximate a mealtime or a user exercising to increase the efficacy of management of the diabetic condition.

Abstract: An application management system may provide, while a user device detects a beacon associated with a medical system, a user of the user device with access to a first set of medical session features of an application executed by the user device. The application management system may also provide, while the user device does not detect the beacon, the user with access to a second set of medical session features of the application, the second set of medical session features being different from the first set of medical session features.

Abstract: Systems and methods facilitate the planning and performance of hip and other surgeries. A computer model of a hip may be generated and displayed. A template of a hip component that replaces a native portion of the hip may be superimposed on the model. Changes in leg length, offset, or anterior-posterior (AP) position as well as a virtual distance between a landmark on the model and a location on the template may be determined. During surgery, a physical distance corresponding to the virtual distance may be obtained. The template may be moved relative to the model to match the physical distance, and new change values in leg length, offset, or anterior-posterior (AP) position may be determined. The new change values may be evaluated, and the surgery may proceed, or the process may be repeated using templates corresponding to alternative components.

Abstract: Techniques and apparatus for generating, performing, and evaluating virtual clinical trials that include clinic modules to represent real-world operational aspects of trial entities are described. In one embodiment, for example, an apparatus may include at least one memory, and logic coupled to the at least one memory. The logic may be configured to generate a plurality of avatars configured to model a health condition associated with a population of patients, and to perform a virtual clinical trial to simulate a course of treatment for the plurality of avatars, the virtual clinical trial to include at least one clinic module associated with at least one event and a probability for the at least one event, the at least one event to model an operational aspect of an entity of the virtual clinical trial. Other embodiments are described.

Abstract: A first data task station which has a first predetermined data content requirement. A data content receiver receives data content. A first activator enables the data content receiver through a data intake of data task station, to associate with a first data content. Movement of the first data content to an automator site of the first data task station, actuates the screener. A second activator of the computer, by operation of the automator, enables, after the first pre-determined data content satisfies the screen against said first pre-determined data content requirement, a second data task station associated with a second predetermined data content requirement. A disabler, by operation of the automator, after the first pre-determined data content satisfies the screen against the first pre-determined data content requirement, disables the further screen of the first data content of said first data station.

Abstract: Methods and systems for improving patient medication adherence by determining when and/or how to intervene with a patient regarding medication adherence are disclosed. An example computer-implemented method for estimating an adherence risk score for a medication taken by a patient includes: receiving, via a network interface from a server configured for determining follow up with patients regarding medication adherence, a request for an adherence risk score for a patient and medication combination, the adherence risk score representing a risk that the patient will not be adherent with taking the medication as prescribed during a time period; obtaining, with one or more processors, pharmacy records for the patient and medication combination; processing the pharmacy records with a machine learning model to determine the adherence risk score; and providing, via the network interface, the adherence risk score to the server.

Abstract: Embodiments described herein provide for recommending radiotherapy treatment attributes. A machine learning model predicts the preference of a medical professional and provides relevant suggestions (or recommendations) of radiotherapy treatment attributes for various categories of radiotherapy treatment. Specifically, the machine learning model predicts field geometry attributes from various field geometry attribute options for various field geometry attribute categories. The machine learning model is conditioned on patient data such as medical images and patient information. The machine learning model is trained in response to cumulative reward information associated with a medical professional accepting the provided/displayed recommendations.

Abstract: An apparatus for generating a user report is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a user profile from a user. The memory instructs the processor to generate a first set of inquiries as a function of the user profile using an inquiry machine learning model. The memory instructs the processor to receive a first set of inquiry responses from the user as a function of the first set of inquiries. The memory instructs the processor to generate a user report as a function of the first set of inquiries and the first set of inquiry responses. The memory instructs the processor to display the user report using a display device.

Abstract: Methods and systems for monitoring of sensor data for processing by machine-learning models to generate event predictions to estimate a risk a medical event are provided. An electronic device or wearable smart device may monitor the output of various sensors to collect data related to a person's activity level, location changes, and communications and may use this information as input to a personalized trained machine-learning model to predict a likelihood of an event.

Abstract: A method, system, and computer program product to prevent misinterpretation of communications via artificial intelligence assistants. The method includes monitoring an artificial intelligence enabled communication. The method also includes analyzing messages, participants, and actions within the communication. The method also includes identifying critical data within the communication. The method also includes determining critical sequence data, where the critical sequence data is a sequence of conversation elements that contains the critical data. The method also includes predicting an uncertainty level for the critical sequence data, where the uncertainty level indicates an amount of uncertainty that the artificial intelligence correctly interprets the critical sequence data. The method also includes determining that the uncertainty level for the critical sequence data is above a threshold uncertainty value, resulting in critical sequence data that is subject to misinterpretation.

Abstract: A computer-implemented method is for providing a clinical information. In an embodiment, the computer-implemented method includes receiving input data including a graph representation of a plurality of disease lesions of a patient; applying a trained function to the input data to generate the clinical information, the trained function being based on a graph machine learning model; and providing the clinical information, the clinical information including at least one information for prediction of the disease progression, survival or therapy response of the patient.

Abstract: A system of generating a pre-operative assessment of a patient includes an assessment system configured to communicate with client electronic devices. The system receives patient information pertaining to a patient, uses the patient information to access one or more medical images associated with the patient, performs one or more image processing techniques on the one or more medical images to identify measurements pertaining to an internal bodily structure of the patient, identifies one or more implant components for the patient, determines whether the one or more implant components are currently stocked, in response to determining that the one or more implant components are not currently stocked, automatically generates and placing an order for the one or more implant components that are not stocked, generates a pre-operative assessment for the patient, and causes the assessment to be displayed on a display device of a client electronic device.