Abstract: A dosimetry assessment method for an organ at risk (OAR) in an esophageal radiotherapy plan includes: acquiring volumetric-modulated arc therapy (VMAT) plan data of a patient with esophageal cancer, and obtaining a distance-target histogram (DTH); subjecting a geometric feature vector of the DTH to dimension reduction to obtain a dimension-reduced geometric feature vector; establishing a deep belief network (DBN) model, and completing training the DBN model; non-linearly fitting a correlation between a dose feature vector and the dimension-reduced geometric feature vector to obtain a dose feature vector with a dimension being identical to a dimension of the dimension-reduced geometric feature vector; and reconstructing the dose feature vector through a decoding layer of an autoencoder structure to obtain a dose feature vector with a dimension being identical to a dimension of the geometric feature vector before the dimension reduction, and finally obtaining a dose-volume histogram (DVH) for predicting the OAR

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A patient monitor (8) includes a display (10). Patient values (38) are obtained for one or more known variables of a risk prediction function (30). One or more unknown variables of the risk prediction function are determined, and at least one hyperplane (40) is defined as values assumable by the one or more unknown variables. Values of the risk prediction function are computed over the at least one hyperplane using the obtained patient values. A visualization template is selected from a database of visualization templates (50) using template selection indices including the risk prediction function and the one or more unknown variables. Using the visualization template, a visualization (52) of the computed values of the risk prediction function over the at least one hyperplane is displayed.

Abstract: Aspects of the present invention relate to facilitating remote expert consultation using a web site that provides for video conferencing between clients and experts who are have been vetted through registration in the system. Some aspects relate to transmission of correlated physiological characteristic data simultaneously with a video conference. Some aspects relate to control of conferencing functions through client and expert web pages.

Abstract: The disclosed embodiments include a computer-implemented method for improving efficacy of diabetes self-management with automated personalized computer engagement that reliably obtains a measurement message and/or contextual information used to determine a suitable insulin dosage of an insulin titration process to treat the diabetic patient. The method includes causing display of a messaging interface that couples the user of the computing device to a chat bot simulating a health coach in a natural conversation to obtain a measurement of the diabetic patient's blood-glucose level. The method includes determining an insulin dosage recommendation to treat the diabetic patient by processing the measured blood-glucose level with an insulin titration algorithm that is personalized for the diabetic patient, and generating an electronic signal configured to cause a corresponding action to be performed by a computing device (e.g., display message of recommended insulin dosage).

Abstract: A medical scan artifact detection system is operable to receive a medical scan of a patient. Artifact detection data is generated by executing an artifact detection function on the medical scan, where the artifact detection data indicates at least one artifact detected in the medical scan that includes a motion artifact or a nipple shadow. A notification is generated for display via a display device, where the notification indicates the at least one artifact.

Abstract: Systems and methods for predicting post-discharge risk are disclosed. A system may include at least one processor configured to access frames of video captured during a specific surgical procedure on a patient and access stored historical data identifying intraoperative events and associated outcomes. The processor may analyze the accessed frames and, based on information obtained from the historical data, identify in the accessed frames at least one specific intraoperative event. The processor may further determine, based on information obtained from the historical data and the identified at least one intraoperative event, a predicted outcome associated with the specific surgical procedure, and output the predicted outcome in a manner associating the predicted outcome with the patient.

Abstract: Systems, methods and computer program products to identify and close gaps in care are provided. Aspects of the present disclosure disclosed and described herein enable a payer may identify gaps in care based on information in the payer database. The payer can send this information to the provider prior to patient scheduling. The scheduling system at the provider can use this information to schedule patients and their medical needs (e.g., medical test, lab tests, imaging tests, etc.) before the next patient appointment. Primary care providers (PCPs) can then be made aware of the gaps in care information, as well as Hierarchical Condition Category (HCC) coding gap information, so they can perform a complete diagnosis using all available clinical data. PCP notes along with the treatment the patient receives can be combined with the RAF (Risk Adjustment Score) shared by the payers to ensure patient is assessed for all relevant conditions during the visit.

Abstract: Medication security and healthcare privacy analytics systems are described that enable users to search for and process stored healthcare environment data. The medication security and healthcare privacy analytics systems receive and correlate data from a plurality of data sources, including medication dispensing systems, healthcare employee records, and patient records, including user behavior or interaction data with the foregoing data sources. The medication security and healthcare privacy analytics systems include graphical user interfaces (GUIs) that enable users to select elements to filter the processed healthcare environment data and generate visualizations of filtered datasets. The visualizations are created using datasets generated by clustering algorithms and can indicate those users from a plurality of users whose interactions with various systems are anomalous (e.g., indicative of unexpected or non-customary user behavior).

Abstract: This disclosure provides a technology for developing alternative protein sources for use in industrial food production. The technology mines natural sources by a process that is done partly in silico. Instead of sampling and testing a vast library of compounds, machine learning and implementation narrows the field of functional candidates by predictive modeling based on known protein structure. Candidate proteins that are selected by this analysis are then produced and screened in a high-throughput manner by recombinant expression and testing to determine whether they have a target function. Multiple cycles of the machine learning, database mining, expression and testing are done to yield potential ingredients suitable for assessment as part of a commercial food product.

Abstract: A system, device, and method for collecting user information. An image and metadata associated with the image are captured utilizing a wireless device. The image and the metadata are associated with a device. The image and the metadata are processed utilizing the wireless device to determine user information associated with the device. The user information is distributed from the wireless device to one or more devices associated with the user information.

Abstract: In one embodiment, one or more non-transitory, computer-readable mediums store computer instructions that, when executed by a processor, cause the processor to receive inputs from one or more request sources. The inputs include user credentials to an electronic medical record (EMR) source provider system. The computer instructions, when executed by the processor, also cause the processor to validate the credentials via communication with the EMR source provider system without an external authorization process, retrieve one or more EMRs from the EMR source provider system after the credentials are validated by the EMR source provider system, and store the one or more EMRs in a single data repository.

Abstract: A method, system, and computer program product provide disease simulation in synthetic projection imagery. The method obtains first medical imaging data of a first imaging type as source imaging data. A second imaging type to be generated from the source imaging data is identified. The method identifies a parameter set for the second imaging type. Second medical imaging data is modeled from the first medical imaging data based on the parameter set. A set of synthetic images is generated from the first medical imaging data based on the modeled second medical imaging data.

Abstract: A system provides artificial intelligence health support for people. The system renders specific, targeted treatments for people by using a flow engine and a conversational service to call one or more conversational modules. The treatments for the people may be tracked. The flow engine and/or one or more of the modules may include different instructions to perform for different programs and/or goals that have been configured. The flow engine and/or one or more of the conversational modules may also include instructions to perform when certain features are active (which may be activated when certain programs and/or goals are configured), when data regarding activity for people are received, and so on. Other modules may be dedicated to particular programs and/or goals. Some modules may determine whether or not to perform various instructions repetitiously, and/or may determine to do so when a priority of a previous instruction is below a threshold.

Abstract: A medical scan artifact detection system is operable to receive, via a receiver, a medical scan of a patient. Artifact detection data is generated by executing an artifact detection function on the medical scan, where the artifact detection data indicates at least one artifact detected in the medical scan. A notification is transmitted, via a transmitter, a client device for display via a display device, where the notification indicates the at least one artifact.

Abstract: Embodiments generally relate to a surgical resource allocation system. Patients seeking a procedure can use the system to identify surgeons and surgical facilities. The patient can then use the system to book the procedure with a selected surgeon at a selected facility. In an example, a patient can indicate a need for a certain procedure. The patient can then provide criteria for a surgeon or facility, such as a desired location, desired time, surgeon rankings, and facility rankings. The system can then identify surgeons and facilities that match the criteria and present them for selection by the patient. The patient can then select the surgeon and facility with which the patient would like the procedure performed. With the surgeon and facility selected, the patient can then choose to book the procedure.

Abstract: Some embodiments described herein provide for a number of portable infusion pumps to be distributed to a pump user via a pharmacy system or like.

Abstract: Systems, methods, and computer-readable media having computer-executable instructions embodied thereon for protocol driven image acquisition are provided. In embodiments, a protocol is received by an image capturing device. The protocol comprises orders from a clinician, a workflow for capturing at least one image, or a combination thereof. At least one field for receiving metadata to be associated with the at least one image allows structured documentation to begin on the image capturing device. The at least one image and associated metadata are communicated to a medical information system. A patient is identified by the metadata or an existing patient to device association and the at least one image is associated with an electronic medical record for the patient.

Abstract: A method for using a health information exchange system which stores patient record data regarding a multiplicity of patients, to serve a first plurality of EMRs each interacting with an EMR community including a set of at least one EMR, the method comprising: for each individual EMR within the first plurality of EMRs, performing a computerized context interception process using a processor to intercept context from the individual EMR and to identify there within an event whereby a health provider using the individual EMR calls up an individual patient's record from said individual EMR; and responsive to identification of the event, using a computerized output device for providing patient record data, pertaining to the individual patient, to the health provider.

Abstract: Analytics relevant to a particular patient are provided within the clinical decision workflow to provide data-driven decision support. When information is received from an end user device at a workflow service that indicates characteristics of a clinical decision support workflow being managed by the end user device for a patient, data is requested from one or more knowledge systems and electronic health record (EHR) data is requested from one or more EHRs. The data and the EHR data is aggregated within the clinical decision support workflow and a recommendation is provided to a clinician at a point of care via the end user device. For example, the recommendation may reveal that a particular care plan is, according to the aggregated data, the model recommendation.