Abstract: Methods, systems, and computer-storage media are provided for determining an individual's second event risk score where the second event risk score represents a likelihood that the individual will experience the second event within a predetermined time period after the occurrence of a first event. Upon occurrence of the first event, a sampling protocol is initiated where an electronic medical record store is accessed on a predetermined schedule to sample a pre-selected set of medical data elements for the individual. Logistic regression analysis is executed on the pre-selected set of medical data elements to generate a second event risk score for the individual. The second event risk score is communicated to a medical professional managing the medical care of the individual, and the individual's electronic medical record is modified to reflect the second event risk score.

Abstract: Embodiments relate to systems and methods that retrieve dialogue data associated with a plurality of utterances. The plurality of utterances include a first utterance. The systems and methods further determine that a target concept, of the dialogue data, is in a first dialogue segment associated with the first utterance. Additionally, the target concept is determined based on the first utterance in the first dialogue segment having a highest weight for relevancy to a knowledge domain. Further, the methods and systems determine a dialogue goal comprising the target concept. Due to the dialogue goal comprising the target concept, a structured link associating the target concept to the dialogue goal is generated.

Abstract: Systems, methods and computer-readable media are provided for identification of patients or family member having genetic disease or probable genetic disease. During or after registration of a patient, parents, grandparents, or siblings of the patient are identified. If it is determined that one of the patient or the parents, grandparents, or siblings of the patient has been assigned with a diagnosis indicating a genetic disease, an alert for genetic disease or probable genetic disease for the patient or family member of the patient is provided. A clinician is then prompted to confirm or rule out the patient or family member inheriting the disease.

Abstract: Technologies are provided for providing a dashboard interface corresponding to one or more datacenter components for information originating from one or more proprietary applications. A selection of information corresponding to the one or more datacenter components is initially received. Upon receiving the selection, an application programming interface (API) call request is made for the information corresponding to the one or more datacenter components from the one or more proprietary applications. As the information corresponding to one or more datacenter components from the one or more proprietary applications is received, it is aggregated and provided within the dashboard interface.

Abstract: Methods, systems, and computer-readable media are disclosed herein for automated identification and alerting of neurodegenerative diseases (NDD). Patient information comprising medical history information is accessed. Using the patient information, one or more risk factors and/or symptoms for a NDD are identified. Using the identified risk factors and/or symptoms, a patient with probable risk for developing the NDD is determined. A notification of the probable risk that the patient develops the NDD is provided.

Abstract: Methods, systems, and computer-readable media are disclosed herein combine randomization functionalities with the machine-learning prioritization of workflows for performance testing. In aspects, a primary workflow having a sequence of user interface steps is input. Testing workflows are generated that represent each variable position of unlocked steps in the sequence of the primary workflow while maintaining the sequential position of any locked steps. These testing workflows are then ingested to a machine learning model that identifies as subset of the testing workflows to prioritize over other. Specifically, testing workflows are prioritized that at least partially match sequence patterns in historical workflow data that is associated with vulnerable computer code. The subset is output and tested by testing engine to generate a report of any vulnerable computer code.

Abstract: Methods, systems and computer storage media are disclosed for providing resources to a platform issue. Embodiments describe associating educational resources and an event resource to resolve the platform issue.

Abstract: Techniques disclosed herein provide a layered healthcare medical stack for embodying the architecture of a subject-centric smart hospital (SH) with customizable smart services. A subject centric matrix hosted on a centralized cloud that is configured to provide interoperability, integration, and customization of services for various healthcare organizations. Data is obtained for each subject to create a subject-specific persona for performing prescriptive and predictive analysis thereby generating alerts and trends based on the current and historical healthcare data of a subject. The subject-centric matrix can enable healthcare facilities to choose any service in any layer without a prerequisite service to be in place first.

Abstract: Methods, systems, and computer-readable media are disclosed herein for a machine learning engine and rule engine that leverage historical and contextual data to intelligently identify, score, and suggest one or more documents for auto-population of a graphical user interface. The machine learning and rule engine employ vectorization and clustering technique to identify, score, and suggest the most factually accurate and contextually relevant documents as selectable candidates for electronic documentation. Further, the selection, rejection, or modification of the candidate documents are ingested by the machine learning engine and/or the rule engine to update a clustering algorithm and/or to update a relevance scoring algorithm, which are then utilized for subsequent instances.

Abstract: A system and method for predicting resource usage using machine-learning models. The method entails collecting a dataset from a variety of data sources such as electronic medical/health records or medical registries. The method identifies if an outreach communication occurred or is scheduled to occur for a subject. A set of features are extracted from the dataset and the method generates derived features from one or more extracted features. The extracted features and generated set of derived features are collated into a candidate feature vector used for training the machine-learning models. The models generate a predicted likelihood of the subject seeking care at the medical facility within a defined time period. Based on the predicted likelihoods of the subjects seeking care at the medical facility, the method predicts an upcoming resource demand at the medical facility. The method generates a recommended action in case predicted resource demand exceeds a threshold.

Abstract: Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care.

Abstract: Computerized systems and methods are provided to intelligently and dynamically manage a data center comprising at least one server and at least one central manager. The central manager is programmed to access the at least one server on a predetermined schedule to determine whether at least one application is functioning properly by determining a functionality level. Alternatively, the central manager determines whether the at least one server is actively used by determining an activity level for the server. Based on the central manager's determinations, the system dynamically adjusts the power level of the server, resulting in reduced power consumption and a reduction in wasted resources and unnecessary processing power in the management of servers in a data center.

Abstract: Decision support technology is provided for use with patients who may be prone to a cardiovascular condition such as acute coronary syndromes. A mechanism is provided to determine a patient's risk for experiencing a cardiovascular ischemic event at a future time interval based on temporal patterns determined using physiological parameters of the patient such as serum or blood uric acid and/or C-reactive protein (CRP). A forecast or score may be determined indicating whether or not temporal patterns merit intervention to prevent occurrence or reoccurrence of ischemic events, or for determining adherence to or efficacy of treatment or preventive interventions. Based on the forecast or score, appropriate response action such as automatically issuing an alert or notification to a caregiver associated with the patient, may be determined, recommended, or implemented.

Abstract: Methods, computer systems, and computer-storage medium are provided for providing closed-loop intelligence. A selection of data is received, at a cloud service, from a database comprising data from a plurality of sources in a Fast Healthcare Interoperability Resources (FHIR) format to build a data model. After a feature vector corresponding to the data model is extracted, a selection of an algorithm for a machine learning model to apply to the data model is received. A portion of the selection of data is utilized for training data and test data and the machine learning model is applied to the training data. Once the model is trained, the trained machine learning model can be saved at the cloud service, where it may be accessed by others.

Abstract: Embodiments herein disclose systems, methods, and computer-readable media for generating CDA (Clinical Document Architecture) documents from Past Healthcare Interoperability Resources (FHIR) APIs utilizing a unified, flexible, cloud-based service that can be leveraged across disparate solutions and/or systems.

Abstract: Methods, systems, and computer-readable media are provided for improving patient safety using virtual observation. A falls risk assessment and a patient safety risk assessment are initially provided within an electronic health record of a patient. A clinician is prompted at a clinician device to provide input to the falls risk assessment and the patient safety risk assessment for the patient. Based on the input, a safety assessment score is determined for the patient. The safety assessment score is provided to the clinician via the clinician device and the clinician is prompted to initiate an order to place a camera in the room of the patient. Based on the order, a virtual sitter may be assigned to the patient to monitor the camera.

Abstract: Methods, systems, and computer-readable media are provided for improving patient safety using virtual observation. A falls risk assessment and a patient safety risk assessment are initially provided within an electronic health record of a patient. A clinician is prompted at a clinician device to provide input to the falls risk assessment and the patient safety risk assessment for the patient. Based on the input, a safety assessment score is determined for the patient. The safety assessment score is provided to the clinician via the clinician device and the clinician is prompted to initiate an order to place a camera in the room of the patient. Based on the order, a virtual sitter may be assigned to the patient to monitor the camera.

Abstract: Methods, systems, and computer-readable media are disclosed herein that utilizes historical changes made to files and methods in computer programming code to predict related files and methods that may be affected by current and/or future changes made to other files and methods. In aspects, when computer code for a particular method is going to be edited, other methods are identified that were changed in previous editing sessions that also included changes to the particular method. Using scoring techniques for the other methods, a recommendation is provided that details the relative strength of whether the other methods are predicted to be affected by any changes made to the computer code for the particular method that is edited.

Abstract: Systems, methods, and computer-readable media are disclosed herein that generate a ternary tree data structure that includes multiple categories (e.g., terminologies) using dynamic array modifications that facilitate sharing of one or more nodes across categories. A plurality of different categories may be added and stored within a single ternary tree data structure such that each categories may be separately queried using the single ternary data structure.

Abstract: The boosting of the weights related to imperfect matches of electronic records from disparate sources is discussed. The imperfect matches may be in primary data (such as a code) and/or in supplemental data between two or more records that correspond to the same person (such as a patient). The imperfect matches are analyzed to determine whether they are sufficient to warrant de-duplication of those imperfect matches in a final combined record for the person. The boosting of the weights may be based upon any of numerous factors, such as various distance measures between the supplemental information as a measure of how different the supplemental information is between the respective records.