Abstract: An automated decision support computer system based on data informatics, includes a first computer readable processing component including a plurality of resource attribute data profiles in which the resource attribute data profiles are generated at least partly from a plurality of electronic computer readable destination records. A second computer readable processing component includes a plurality of patient classification attribute data profiles in which the patient classification attribute data profiles are generated at least partly from a plurality of computer readable electronic health records. The automated decision support computer system also includes a decision support classification computer engine for scoring and ranking the plurality of resource attribute data profiles with the plurality of patient classification attribute data profiles to generate a computer readable evaluation attribute data profile for display on a computer screen.

Abstract: Example methods and systems for radiotherapy treatment planning based on continuous deep learning are provided. One example method may comprise: obtaining a deep learning engine that is trained to perform a radiotherapy treatment planning task based on first training data associated with a first planning rule. The method may also comprise: based on input data associated with a particular patient, performing the radiotherapy treatment planning task using the deep learning engine to generate output data associated with the particular patient; and obtaining modified output data that includes one or more modifications to the output data generated by the deep learning engine. The method may further comprise: based on the modified output data, generating second training data associated with a second planning rule; and generating a modified deep learning engine by re-training the deep learning engine using a combination of the first training data and the second training data.

Abstract: Systems and methods for fabricating aligners include a central processing system and a dental aligner fabrication system. The central processing system includes or provides a patient intake portal corresponding to a first dentist or orthodontist who conducts an intraoral scan or administers an impression of the patient, a treatment plan portal corresponding to a treatment plan computing system which generates treatment plan data for the patient, and an approving dental portal corresponding to a second dentist or orthodontist who approves the treatment plan. The fabrication system includes fabrication equipment which produces one or more dental aligners based on the treatment plan data.

Abstract: A dehydration amount prediction method for hemodialysis and an electronic device using the same are provided, and the method includes the following steps. Physiological data and hemodialysis treatment data of a first patient are obtained. The first patient is determined to belong to one of groups, wherein the groups are respectively associated with a plurality of prediction models. A target prediction model corresponding to the one of the groups is selected from the prediction models. The physiological data and the hemodialysis treatment data are provided to the target prediction model to generate a recommended dehydration amount by the target prediction model.

Abstract: In some examples, authorized user profiles and generic models are generated using historical interaction data of authorized users. The authorized user profiles provide an experience profile for authorized users. The generic models are data models used to model authorized user actions. The authorized user profiles and generic models are used to generate support elements. The support elements are presented to authorized users and feedback is collected. Based on the feedback, the authorized user profiles and generic models are updated.

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: Described herein are database systems including one or more remote analytical instruments operably connected to one or more servers. The instruments can transmit rich data to the servers, and the one or more servers can compile a database of the rich data. One or more processors associated with the servers can be configured to execute a data analytics program on the database to identify a stochastic phenomenon or to process the data and present in real-time at a location of the one or more instruments comparison information about the instruments.

Abstract: Disclosed embodiments provide techniques for identifying gaps in health management data, and combining an estimated data subset with the health management data to fill in gaps in the health management data. The health management data can be derived from a wearable electronic fitness tracking device such as a smart watch or pedometer. The estimated data subset can be derived based on historical data for the individual, average data for a demographic group, crowdsourced, or estimated based on user profile information, and/or portable electronic device information, such as from a smartphone belonging to a user. The estimated data subset is combined with the health management data to form a revised health parameter dataset. The estimated data subset may be sent to a wearable electronic fitness tracking device, which causes the wearable electronic fitness tracking device to initiate a fitness program adjustment based on the revised health parameter dataset.

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.

Abstract: Embodiments of the present systems and methods may provide techniques that provide enrichment of semantic graphing with relations that can enable a higher resolution of the semantic relationships and enable a more accurate prediction of new relations in the graph. For example a method for drug discovery and drug repositioning may comprise generating semantic relationships, at the computer system, based on data relating to a plurality of aspects of drugs and pharmaceutical compounds, generated semantic relationships represented in the form of a semantic graph, learning, at the computer system, new relations among the semantic relationships in the semantic graph using Denoising Autoencoders to process the semantic graph, and generating, at the computer system, predictions for drug discovery and drug repositioning based on the semantic relationships, including the newly found relations.

Abstract: A system for generating a comestible plan to manage musculoskeletal system disorders is disclosed. The system comprises a computing device configured to receive an input comprising physiological data. Computing device may generate a physiological data classifier, Computing device may classify, using the physiological data classifier, the physiological data to a class of physiological data relating to musculoskeletal disorders. Computing device may extract a plurality of biological determinants of a disease state from the physiological data, wherein the plurality of biological determinants includes at least one biological determinant related to at least one disorder located in musculoskeletal system. Computing device may determine a biological determinant concentration. Computing device may identify a musculoskeletal system disorder based on the at least one biological determinant and the biological determinant concentration.

Abstract: Disclosed embodiments provide techniques for identifying gaps in health management data, and combining an estimated data subset with the health management data to fill in gaps in the health management data. The health management data can be derived from a wearable electronic fitness tracking device such as a smart watch or pedometer. The estimated data subset can be derived based on historical data for the individual, average data for a demographic group, crowdsourced, or estimated based on user profile information, and/or portable electronic device information, such as from a smartphone belonging to a user. The estimated data subset is combined with the health management data to form a revised health parameter dataset. The estimated data subset may be sent to a wearable electronic fitness tracking device, which causes the wearable electronic fitness tracking device to initiate a fitness program adjustment based on the revised health parameter dataset.

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: This patent disclosure provides various embodiments of combining multiple modalities of non-text surgical data of different formats, in particular in forms of videos, images, and audios in a meaningful manner so that the combined data from the multiple modalities are compatible with text data. In some embodiments, prior to combining the multiple modalities of surgical data, multiple segmentation engines are used to segment and convert a corresponding modality of surgical data into a corresponding set of metrics and parameters. The multiple sets of metrics and parameters corresponding to the multiple modalities are then combined to generate a combined feature set. The combined feature set can be provided to a data analytics tool for performing comprehensive data analyses on the combined feature set to generate one or more predictions for the surgical procedure.

Abstract: An electronic computing system creates a treatment plan and provides safe and accurate treatment recommendations by obtaining an input image of a face; comparing, using a pattern recognition process, one or more aspects of the input image to corresponding aspects of a plurality of reference images; obtaining, based on a result of the comparing, supplemental information associated with one or more additional characteristics of the face; and creating a treatment plan based on the input image and the additional characteristic.

Abstract: Mechanisms are provided that implement a plurality of heterogeneous causality models and a metaclassifier for predicting a likelihood of causality between a drug and an adverse event (AE). The plurality of heterogenous causality models process drug information for the drug to generate a plurality of risk predictions for a drug and AE pair. The risk predictions include at least one of a risk score or a risk label indicating a probability of the AE occurring with use of the drug. The plurality of heterogenous causality models provide the risk predictions, associated with the drug and AE pair, to a metaclassifier which generates a single causality score value indicative of a probability of causality between the drug and the AE, of the drug and AE pair, based on an aggregation of the risk predictions from the plurality of heterogenous causality models. The metaclassifier outputs the single causality score value in association with information identifying the drug and AE pair.

Abstract: Mechanisms are provided that implement a plurality of heterogeneous causality models and a metaclassifier for predicting a likelihood of causality between a drug and an adverse event (AE). The plurality of heterogenous causality models process drug information to generate a plurality of risk predictions for a drug and AE pair. The risk predictions include a risk score or a risk label indicating a probability of the AE occurring with use of the drug. The plurality of heterogenous causality models provide risk predictions, associated with the drug and AE pair, to a metaclassifier which generates a single causality score value indicative of a probability of causality between the drug and the AE, of the drug and AE pair, based on an aggregation of the risk predictions from the plurality of heterogenous causality models. The metaclassifier outputs the single causality score value in association with information identifying the drug and AE pair.

Abstract: A method, computing device and computer program product are provided to define a level of assurance in one or more links between patient records that relate to the same person based upon external information. In the context of a method, one or more links are identified between a patient record associated with a person and pre-existing patient records. Each pre-existing patient record has a plurality of demographic attributes associated with a patient. The method identifies one or more links based upon an analysis of the demographic attributes of the person and the respective patients associated with the pre-existing patient records. The method receives external information regarding a relationship between the person and the respective patients associated with the pre-existing patient records or regarding confirmation of an identity of the person. The method also defines a level of assurance in the respective one or more links based upon the external information.

Abstract: Systems and techniques for monitoring, predicting and/or alerting for census periods in medical inpatient units are presented. A system can perform a first machine learning process to learn patterns in patient flow data related to a set of patient identities and a set of operations associated with a set of medical inpatient units. The system can also perform a second machine learning process to detect abnormalities associated with the patterns in the patient flow data. Furthermore, the system can determine patient census data associated with a prediction for a total number of patient identities in the set of medical inpatient units during a period of time based on the patterns and the abnormalities. The system can also generate an alert for a user interface in response to a determination that the patient census data satisfies a defined criterion.

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.